https://www.whitehouse.gov/briefing-room/presidential-actions/2023/10/30/executive-order-on-the-safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence/ It looks like content will have to be labeled, showing if it's AI-generated or not. And special rules will apply to: any model that was trained using a quantity of computing power greater than 1026 integer or floating-point operations, or using primarily biological sequence data and using a quantity of computing power greater than 1023 integer or floating-point operations; and any computing cluster that has a set of machines physically co-located in a single datacenter, transitively connected by data center networking of over 100 Gbit/s, and having a theoretical maximum computing capacity of 1020 integer or floating-point operations per second for training AI. Also, easier visas for "AI talent". submitted by /u/we_are_mammals [link] [comments]  ( 9 min )

I came across this interesting problem in RAG, what I call Relevance Extraction. After retrieving relevant documents (or chunks), these chunks are often large and may contain several portions irrelevant to the query at hand. Stuffing the entire chunk into an LLM prompt impacts token-cost as well as response accuracy (distracting the LLM with irrelevant text), and and can also cause bumping into context-length limits. So a critical step in most pipelines is Relevance Extraction: use the LLM to extract verbatim only the portions relevant to the query. This is known by other names, e.g. LangChain calls it Contextual Compression, and the RECOMP paper calls it Extractive Compression https://twitter.com/manelferreira_/status/1713214439715938528 Thinking about how best to do this, I realized i…  ( 10 min )

I am working for a months on facial recognition system. I have 500 classes with 10 template for each class. I have applied almost and tested almost every model. Some of them are dlib 128 dim. Facenet (512), Insightface, Arcface. But none of them reduced the rate of false positive. I also fine tuned those models, I also trained a clasifier and I reached to 91 percent of accuracy with a very low quality images and also have a backlight effect. But the what I want my setup to be 99 percent which correctly classifies the person with a high similarity on true positive and a low similarity around 0.1 or 0.2 for an unknown class. Now my problem is that similarity is also high with unknown almost around 99 and I also used some distance metrics and it also not helping out. Note: the environment is a real world environment and I used CCTVs at a place where hundreds of unknown people visit daily. submitted by /u/No_Garbage9512 [link] [comments]  ( 9 min )

With a computation graph typically used to represent NNs, where nodes represent operations and edges represent data dependencies, is it possible to represent in-place operations? submitted by /u/thanrl [link] [comments]  ( 9 min )

I recently wrote a blog on evaluating whether your LLM applications are following required guidelines: https://uptrain.ai/blog/lost-in-translation-the-critical-impact-of-neglecting-guideline-adherence-in-llms?utm_source=reddit&utm_medium=reddit&utm_campaign=reddit Please let me know your feedback in the comment section submitted by /u/Vegetable-Skill-9700 [link] [comments]  ( 9 min )

Blog: https://together.ai/blog/redpajama-data-v2 Hugging Face: https://huggingface.co/datasets/togethercomputer/RedPajama-Data-V2 GitHub: https://github.com/togethercomputer/RedPajama-Data Description: RedPajama-V2 is an open dataset for training large language models. The dataset includes over 100B text documents coming from 84 CommonCrawl snapshots and processed using the CCNet pipeline. Out of these, there are 30B documents in the corpus that additionally come with quality signals, and 20B documents that are deduplicated. submitted by /u/APaperADay [link] [comments]  ( 9 min )

Paper: https://arxiv.org/abs/2310.15421 Code: https://github.com/skywalker023/fantom Blog: https://hyunw.kim/fantom/ Abstract: Theory of mind (ToM) evaluations currently focus on testing models using passive narratives that inherently lack interactivity. We introduce FANToM 👻, a new benchmark designed to stress-test ToM within information-asymmetric conversational contexts via question answering. Our benchmark draws upon important theoretical requisites from psychology and necessary empirical considerations when evaluating large language models (LLMs). In particular, we formulate multiple types of questions that demand the same underlying reasoning to identify illusory or false sense of ToM capabilities in LLMs. We show that FANToM is challenging for state-of-the-art LLMs, which perform significantly worse than humans even with chain-of-thought reasoning or fine-tuning. ​ https://preview.redd.it/mxb85o2vkexb1.png?width=1367&format=png&auto=webp&s=8749cddd15e6740e69ae47ef5edf3a1da96d89c2 submitted by /u/APaperADay [link] [comments]  ( 9 min )

I never hear much about A/B testing in the ML community. Do you all tend to A/B test your changes? I have to imagine this is becoming more prevalent with LLMs and prompts. In my experience, every model change we made was through an A/B test. There is some literature in our domain that offline metrics may not correlate to online metrics, so we looked at both very closely. My team was heavily involved in A/B testing as well as managing the data pipeline from those exposures to create training data for the models. What do your experience look like? submitted by /u/mtbarta [link] [comments]  ( 9 min )

[P] Dm for further info but all in all its abt adding an extra feature (feature engg.) To our ML model to detect politeness in a language submitted by /u/PrudentFly8507 [link] [comments]  ( 9 min )

Recently, niji journey launched a mobile app, and there, they talked about how hard it is for people who are new to the concept of generative AI art to get started, and some of the workarounds they came up with. https://sizigi.notion.site/Kindling-the-Spark-of-Inspiration-d8edd06cede04401b4efba8324005cf3?pvs=4 Thought it would be interesting to share, especially with how they try to push forward the ideas from other users as a font of knowledge kind of thing. submitted by /u/kalmatos [link] [comments]

Hey everyone, I've been working on fine-tuning Whisper using generated transcribed audio data. Before I began the fine-tuning, I evaluated the base model's accuracy on a test set, which showed: Test_accuracy: WER = 23.078% I then fine-tuned the model for 3,000 steps using a dataset of 1,000 samples: 700 for training and 300 for testing. This amounts to roughly 4 hours of data, with each audio clip being around 30 seconds in duration. However, post-fine-tuning, the WER started off at 30%, which is higher than the base model's 23.078%. Intuitively, I'd expect the WER to start lower after fine-tuning. Does anyone have insights or suggestions on what might be causing this discrepancy? Any advice would be greatly appreciated! https://preview.redd.it/b619v95rgdxb1.png?width=906&format=png&auto=webp&s=fa17f311301919ccfcd7a8cea47f47478e1cd91d Edit: Solved the issue. submitted by /u/stoicbats_ [link] [comments]  ( 9 min )

Let’s say I already have a ranking method (a learning to rank approach) to sort search results (items) based on their utility to user. However, I also want to make sure certain groups of items get enough exposure across all the searches. I have defined target exposure for each group but I’m struggling to find a method that would balance utility and this exposure. Particularly because utility can be defined for a given search and item, but the group target exposure is only defined for an aggregated set of search results. Any ideas for a multi objective optimization method or even a post hoc re-ranking approach? submitted by /u/MLE-MAP [link] [comments]  ( 9 min )

Please read, clap and follow: https://medium.com/@andysingal/from-data-architect-to-machine-learning-enthusiast-e132f6cd35fc?sk=d7d2a04a08ca7f328c4ae7ee3769ec50 submitted by /u/Fit_Maintenance_2455 [link] [comments]

Hi! Looking for any insights/direction on high-quality (near pixel-perfect) segmentation datasets for benchmarking various segmentation models in a way that others can reproduce - not sure if these exist or if anyone has specific examples they have worked with? Working with the usual suspects for segmentation like ADE20K and Cityscapes I have seen results using the pre-trained models on these datasets are a bit disappointing wrt high quality (clean edges, label consistency) segmentation - even models considered SOTA in 2023 like OneFormer/SegFormer/etc. Though my hypothesis is that this is largely due to the label approximations (many are drawn as rough polygons) and labeling inconsistencies building these large datasets with many classes. My observations and personal experience is many private companies have custom, high-quality segmentation data they have produced internally at high financial/time cost and thus are reluctant to open source and share, but hoping there are some hidden gems out there I don't yet know about... submitted by /u/FocalAIDev [link] [comments]  ( 9 min )

Hi all I am, working on a project to predict velocity vectors from boundary conditions... I have carried out a simulation of an office on Ansys I have x,y,z velocity vectors I have repeated the simulation for 100s of ac inlet and outlet velocities as input data for each set of 3D-Velocity vectors.Example of the dataset for each inlet, outlet permutation, the dataset for each permutation is around 1 million coordinates + xyz velocity vectors long. *** I now want to predict the x,y,z velocity vectors at each location based on the AC inlet and outlet velocity *** How do I organise the data best for a CNN/other ANN? Any other tips of the CNN architecture? submitted by /u/No_Range3026 [link] [comments]  ( 9 min )

I just discovered the FP8-LM paper from MS: [2310.18313] FP8-LM: Training FP8 Large Language Models (arxiv.org). This is their repo link: Azure/MS-AMP: Microsoft Automatic Mixed Precision Library (github.com) paper abstraction My Key Takeaways: The whole-loop for FP8 “GPT-style” large model training is successfully done by FP8-LM team, including data cleaning, infrastructure development, model pretraining, alignment (SFT, RS, RLHF, etc.) Their FP8 mixed-precision training framework got 42% reduction in memory usage, and ran 64% faster than BF16 Megatron-LM; also faster than Nvidia Transformer Engine by 17% ​ https://preview.redd.it/jeaadb1jncxb1.png?width=793&format=png&auto=webp&s=2175969217ff0ff3c8149d17b8011408f4f84c91 It is thrilling to think about that we can scale up the already gigantic model size by 2.5x without needs for more GPU memory…and this can be achieved with NO performance degradation on a wide range of benchmarks as demonstrated in the paper. ​ https://preview.redd.it/vlu6o5cnncxb1.png?width=1389&format=png&auto=webp&s=ed97ea1431f8d9a2900490812f23131681c788f8 ​ https://preview.redd.it/murtte9oncxb1.png?width=1289&format=png&auto=webp&s=6ebd242d69380f2bd95dcd2fa2afe18d7c4b3667 submitted by /u/TensorTamer [link] [comments]  ( 9 min )

Hey, r/MachineLearning, it’s Dean from DagsHub 🐶 We recently had the opportunity to work with a domain expert in creating a machine learning model and we thought we should share what we learned in the process. A dentist reached out to us to help him create a machine learning model, which could segment teeth in panoramic X-rays. He had some data pre-labeled, but the vast majority of his dataset was unlabeled. Since labeling these X-rays is a time consuming process and requires domain knowledge, we decided to use Active Learning. Following our success in creating an Active Learning pipeline in a Jupyter Notebook using Data Engine, we created a new Tooth Fairy project, which expands on that and brings even more capabilities into the notebook. https://dagshub.com/blog/active-learning-with-domain-experts-a-case-study/ Check out our post and learn: Why and when you should use Active Learning How to efficiently work with domain experts (and mistakes to avoid!) What a real use-case Active Learning pipeline looks like, by checking out the accompanying repo We value your thoughts and feedback! Looking forward to hearing from you all! submitted by /u/PhYsIcS-GUY227 [link] [comments]  ( 9 min )

Hi everyone, Need some ideas to bounce off. I have several medical codes, let’s name them A, B, C and D. Each medical code consists of multiple clauses, say, 1.1, 1.2 and so on. I want to create a model (?) where a text input of a textual clause will show up all other related clauses from different medical codes. For example, if I input clause 3.2 from medical A, I want the output to show up the related/similar clauses from code B, C and D. I have thought of using something like a Retrieval Augmented Generation for this, but anyone has any better ideas regarding this topic? Could a language model do something about this? Thanks! submitted by /u/plsendfast [link] [comments]  ( 9 min )

Hello. I have historical data of real estate. Includes attributes like price, revenue, maintenance, maintenance debt etc. I had two ideas for the data previously, to predict future real estate price and to use some clustering algorithm to put the properties into categories (good, average bad or something like that). Now I got this idea to generate clusters for any given point in time and use that history of clusters to predict migration of properties between clusters. I am aware of inter cluster migration estimation but that seems to be a prediction of how points shift inside a cluster with the introduction of new data points rather than a timeline of the movement of points in the cluster. Writing this I'm also thinking it might be possible to simply treat the history of clusters as a category to be predicted e.g. given the history of property X that is in category A the prediction is that in 6 months it's heading for cluster B. Does anyone have experience with similar work? Are there papers I am missing? Thanks in advance. submitted by /u/arachnarus96 [link] [comments]  ( 9 min )

So! I am a ML newbie and was wondering if one of you pros can help me out on my learning journey (tool use = google colab). I have a csv file containing loan data where each row is a customer that applied for a loan. One of the columns is called TARGET and it shows whether the customer's loan request was approved or not. All sorts of data points are captured e.g. age, gender, salary, employment details like industry, assets, etc. I've done cross validation and found that GradientBasedClassifier and LGBM perform the best. Cross validation also tells me that their accuracy is between 68%-70%. My problem is that I SUCK at hyper param optimisation. How do you go from 68 to +80%??? Or 90%? For the curious ones, here is the dataset: https://drive.google.com/file/d/1IKNVstck6gnXvfGS-mVRMAE1RFrDNUgZ/view?usp=sharing submitted by /u/Critical_Ad_1205 [link] [comments]  ( 9 min )

I want to create several labels for each of these images, such as no. of bedrooms, bathrooms, etc. I have a large dataset of 2000 images, each which have XML which contains text that labels each room with the ID.How do I automate the process, and create a dataset that includes all the features for the labels, so that it's ready for machine learning? https://svgshare.com/i/z53.svg submitted by /u/pranksbanker [link] [comments]  ( 9 min )

submitted by /u/Jean-Porte [link] [comments]  ( 8 min )

I want to know the tools and methods you use for the observability and monitoring of your ML (LLM) performance and responses in production. submitted by /u/Ok_Cartographer5609 [link] [comments]  ( 9 min )

Hey everyone! I recently delved deep into the fascinating world of Super Resolution and put together a tutorial covering both SRGAN and ESRGAN. Plus, for my fellow gamers out there, I've included a comparison between DLSS and VSR in gaming scenarios. For those interested in the technical details, there's also a hands-on PyTorch walk-through of ESRGAN. I put a lot of effort into making the content approachable and informative, so whether you're a newbie or a seasoned pro, there should be something for everyone! Check out the video here: https://youtu.be/Z0jl8YM5kzU Would love to get your feedback, thoughts, or any insights you might want to share! Cheers! 🍻 Disclaimer: This video provides a broad overview of SuperResolution. NVIDIA's DLSS technology involves real-time rendering complexities that may not be fully detailed. For a deeper technical dive, we value and encourage viewers own research. We can have a separate video on DLSS as well. submitted by /u/Worldly-Inflation-92 [link] [comments]  ( 9 min )

Can you tell who someone is from their telephone number? That’s kinda the point of telephone numbers, to let you contact someone. And indeed telephone number is one the 18 identifiers under HIPAA Safe Harbor. But whether any piece of information allows you to identify someone depends on context. If you don’t have access to […] Identifiers depend on context first appeared on John D. Cook.  ( 5 min )

Hello everyone, I've recently started tinkering with ai art generators and could use some advice. I'm on android and currently using magir app, I'm wondering if there are any free ai art generators with no restrictions/ have nsfw etc that doesn't come with a pay wall, magir has lifetime pro for $40, I'm still learning but certainly see the potential so I'm asking for group knowledge please and thank you in advance. submitted by /u/gundamt51 [link] [comments]

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TL;DR - Title. I’m a grad student and had to do some work on a study on the socio-economic impacts of AI and developing an interactive educational platform to ease learning for visually impaired students. I’ve had to do ‘delegate’ some work to chatgpt, and the results have been kinda unimpressive. I shared this with a colleague and he said Ai results are like that, and if I wanted better results I could mix and match, or use other ai tools to ‘boost’ (?) gpt. Is this a viable strategy, or do I have to make do with whatever I have? submitted by /u/CrispOriginality [link] [comments]

A tech expert warns that unrestrained AI development by a few tech companies is endangering humanity's future. The expert calls for AI safety standards and regulation to prevent the reckless development of powerful AI systems. In a policy document, AI experts argue that governments should have the authority to halt the development of exceptionally powerful AI models. Concerns about the development of artificial general intelligence, which can perform tasks at or above human levels, are also raised. The article mentions the investments made by Amazon, Microsoft, Alphabet, and Facebook's Meta in AI and cloud computing. Source : https://www.theguardian.com/technology/2023/oct/26/ai-artificial-intelligence-investment-boom submitted by /u/NuseAI [link] [comments]

The article discusses the existence of Google Tensor in light of the impressive AI capabilities of the Snapdragon 8 Gen 3 chip. While Tensor has been praised for bringing AI breakthroughs to Pixel phones, some argue that many of its AI features actually rely on an internet connection and offload tasks to the cloud. In comparison, the Snapdragon chip can perform on-device AI tasks, such as generating images, quickly and without the need for an internet connection. Despite the criticisms, one argument for Tensor is its longer support timeline and the ability for Google to focus on specific AI applications. However, after seeing Qualcomm's AI demos, the article questions the validity of Tensor's main pitch for AI. Source : https://9to5google.com/2023/10/29/snapdragon-8-gen-3-google-tensor-ai/ submitted by /u/NuseAI [link] [comments]

Hey! I have a specific background that I would like to use together with various product pictures that have varying backgrounds. Essentially I am looking for a tool that can remove backgrounds of my product images, then add a certain background I have and do the shadows and lightning well on the background. Any ideas? For example in adobe firefly I can remove the background and generate a new one with a prompt but it’s not exactly like the background I would like to use and I need it to be 95% similar at least. Thanks in advance! submitted by /u/Herrpadda [link] [comments]

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The Ai Pin, the new gadget / wearable device / projector / thing from the secretive startup Humane, might cost as much as $1,000 and may require a monthly subscription for data, according to The Information.[1] OpenAI to release updated version of ChatGPT that gives users access all GPT-4 tools – including browsing and DALL·E 3 – without switching.[2] MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations.[3] Vietnam is at the ‘leading edge’ of AI developments in emerging Southeast Asia: JPMorgan.[4] Sources: [1] https://www.theverge.com/2023/10/27/23935644/humane-ai-pin-price-subscription [2] https://www.searchenginejournal.com/new-version-of-chatgpt-gives-access-to-all-gpt-4-tools-at-once/499607/ [3] https://mimicgen.github.io/ [4] https://www.cnbc.com/video/2023/10/30/vietnam-ahead-in-ai-developments-in-emerging-southeast-asia-jpmorgan.html submitted by /u/Excellent-Target-847 [link] [comments]

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I run a small independent record label. Of course, content is important and I am looking at ways for maximizing my content on a budget. Photographers are very expensive and so is having a videographer/photographer at each show for the artist to take pictures. Can anyone suggest a good AI program where I can use my artists as models and could potentially create good photo content? Any advice or input would be helpful! Thank you. submitted by /u/mc7eunit [link] [comments]

I created a custom environment to use it with RL algorithms. My custom environment provides the observations and receives rewards. I test my environment with my custom RL algorithm (Policy gradient) and the algorithms in stable-baselines3. My custom algorithm and DQN from stable-baselines3 works. However, if I use baselines with my custom algorithm or use PPO or A2C from sb3, I get: RuntimeError: could not create a primitive descriptor for a matmul primitive Any idea why this is happening? Extra details: On the custom algo error occurs here: "/app/src/networks/critics.py", line 40, in forward return self.network(obs) submitted by /u/FragrantCockroach8 [link] [comments]

Hello everyone I am taking reinforcement learning course. I am studying the course from two different books. In one of them it says R(s,a) -> [0,1] in other it says -> R. I am confused about the linitation of the reward function. This is the link of book [0,1] (for infinite mdp for finite mdp it says [0.1] rh)(s,a), adds trajectory ) . I want learn that is one of them false or am i missing smth submitted by /u/karakobra1 [link] [comments]

A beginner friendly introduction to Reinforcement Learning and the intuitions behind it… with four seminal projects that revolutionized the field of AI and RL. submitted by /u/AvvYaa [link] [comments]

I put up a post about this earlier but soon realized that I hadn't given much information. ​ In order to create my custom gym environment, I did the following things - I went over the documentation given over here. I cloned this repository. In the folder `gym-examples/gym-examples`, I created the file - `my_test.py`. The contents of the file are given over here -``` ​ import gym env = gym.make('gym_examples/GridWorld-v0') print("Did this work?")``` However, when I try to run this file, I get the error - ``` File "D:\custom_env\gym-examples\my_test.py", line 2, in env = gym.make('gym_examples/GridWorld-v0') ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\thoma\anaconda3\envs\env_torch\Lib\site-packages\gym\envs\registration.py", line 569, in make _check_version_exists(ns, name, version) File "C:\Users\thoma\anaconda3\envs\env_torch\Lib\site-packages\gym\envs\registration.py", line 219, in _check_version_exists _check_name_exists(ns, name) File "C:\Users\thoma\anaconda3\envs\env_torch\Lib\site-packages\gym\envs\registration.py", line 187, in _check_name_exists _check_namespace_exists(ns) File "C:\Users\thoma\anaconda3\envs\env_torch\Lib\site-packages\gym\envs\registration.py", line 182, in _check_namespace_exists raise error.NamespaceNotFound(f"Namespace {ns} not found. {suggestion_msg}") gym.error.NamespaceNotFound: Namespace gym_examples not found. Have you installed the proper package for gym_examples? ``` ​ https://preview.redd.it/f0m02hky4dxb1.png?width=521&format=png&auto=webp&s=9fcd6eb7bedb70273f011a02000d15eb7ed309df submitted by /u/Academic-Rent7800 [link] [comments]

I'm looking for research in the field of Deep Reinforcement Learning, where one agent controls the "action" of another, where the other executes that said action. For example, a master agent (lets say air traffic controller) tells another agent (lets say an airplane) to go from Point A to Point B, where the second agent (airplane) needs to learn how to maneuver from Point A to Point B, and the master agent (air traffic controller) needs to learn to schedule multiple other agents, or needs to learn an optimal path for a single airplane. ​ In essence, can anyone point me to research with multiple agents, where one is regarded as "master" agent and the others as the "pawns"? submitted by /u/MyActualUserName99 [link] [comments]

Hello everyone, I'm new to RL and have been trying to set up an environment to learn to play Metroid 2: Return of Samus on the Game Boy using the pyboy emulator and sb3_contrib.QRDQN as the learning algorithm. https://github.com/lixado/PyBoy-RL was used as a base to build on and change. My reward function rewards the AI for progressing to a new area (every ~200 x/y coordinates is stored as an area) to encourage it to explore. I've had some progress so far and have been able to consistently produce a model that can get out of the starting area. The observation I was originally working with was a 16x20 array of tiles and sprites from pyboy._game_area_np. pyboy sets this observation shape to be a 16x20 MultiDiscrete with each element containing integers from 0 to 384. This was then transform…

Hello everyone, I have some questions about the application of DRL in production scheduling. Considering that we have N jobs, each consists of X operations and each operation can be processed on a set of machines. Is it appropriate to create a single agent that selects an action once a machine completes the in-process operation, where the action consists in selecting the next operation to be processed in this machine ? so at each decision time (i.e., when a machine requests processing), the agent selects an operation from the available operations of this machine. I'm wondering about the feasability because in the most papers I've seen so far, once an operation is completed, rather than selecting an action for the idle machine, the agent selects the next operation of the job and assign it to the closest available machine. Thank you! submitted by /u/GuavaAgreeable208 [link] [comments]

Amazon Bedrock is a fully managed service provided by AWS that offers developers access to foundation models (FMs) and the tools to customize them for specific applications. It allows developers to build and scale generative AI applications using FMs through an API, without managing infrastructure. You can choose from various FMs from Amazon and leading […]  ( 8 min )

We are excited to announce a simplified version of the Amazon SageMaker JumpStart SDK that makes it straightforward to build, train, and deploy foundation models. The code for prediction is also simplified. In this post, we demonstrate how you can use the simplified SageMaker Jumpstart SDK to get started with using foundation models in just a couple of lines of code.  ( 7 min )

Two roads diverged in a wood, and I;I took the one less traveled by,And that has made all the difference. — Robert Frost At certain points in the evolution of enterprise artificial intelligence, there’s been a fork in the road. The road less traveled has suggested a different route to a more satisfying kind of… Read More »FAIR knowledge: The key precondition for trusted generative AI The post FAIR knowledge: The key precondition for trusted generative AI appeared first on Data Science Central.  ( 21 min )

A research paper released today describes ways generative AI can assist one of the most complex engineering efforts: designing semiconductors. The work demonstrates how companies in highly specialized fields can train large language models (LLMs) on their internal data to build assistants that increase productivity. Few pursuits are as challenging as semiconductor design. Under a Read article >  ( 6 min )

Teachers are the backbone of any educational system. They are not just educators; they are indispensable navigators, mentors, and leaders. Teachers around the world face many challenges, which vary from country to country or even within a city or town. But some challenges are universal, including time management, classroom organization, and creating effective lesson plans. […] The post Teachers in India help Microsoft Research design AI tool for creating great classroom content appeared first on Microsoft Research.  ( 12 min )

Complimentary approaches — “HighLight” and “Tailors and Swiftiles” — could boost the performance of demanding machine-learning tasks.  ( 11 min )

The SecureLoop search tool efficiently identifies secure designs for hardware that can boost the performance of complex AI tasks, while requiring less energy.  ( 10 min )

Two studies find “self-supervised” models, which learn about their environment from unlabeled data, can show activity patterns similar to those of the mammalian brain.  ( 11 min )

submitted by /u/firef1y1 [link] [comments]  ( 8 min )

Hey people, I'm new to the AI world (been programming (python) for 4 years but never worked with AI stuff), I need to extract some specific info from documents, I'm reading about NLP and all this stuff but still figuring out which method(s) should I use to make this works, any recomendations of which methods to use? submitted by /u/luiz200411 [link] [comments]  ( 9 min )

I am confused! I am trying to list all the types of SVMs but every website says different things! Could you help me and list all the SVMs types with the reference? Thank you submitted by /u/_LadyBee [link] [comments]  ( 9 min )

Hey everyone! I need some advise on creating a conversational chatbot for my University as my Final Year Project (FYP). 2024 will be last year for my BSCS degree and we have to build an application or something in the last year. So, I thought of creating a chatbot (just like GPT) to help students (who have admission queries). Most of the time, students or parents will have to call University for various questions and then they have to wait to ACTUALLY talk to the admins office people. Now, talking in terms of coding/programming, I have created a basic PDFbot by using LLama2, Huggingface and Pinecone. Its very very easy and yes its fairly inaccurate too. The PDF that I am using rn will be replaced by the dataset that I gather in order to create the bot for my Uni, but it will also be inac…  ( 10 min )

I would like to understand if anyone has implemented generative AI without using openAI and if so which open source you have used and how successful it has been so far We have enterprise level incident data, relevant documentation etc that users will search about and need to generate responses using generative ai. Is it possible to do this without relying on open AI at all submitted by /u/leaderof13 [link] [comments]  ( 9 min )

I have been tasked with predicting the demand for products over the course of the next 12 months for a b2c furniture business in-order to help with stock management. I currently just work as a Data Analyst but looking to move into data science so my thought is to make this a valuable piece of work that helps the business tremendously (maybe being a little naive). Background: There are 1.3k unique products, which all vary in popularity (quantity is small 0-16 units per month, per product). We have procured which have been selling for 5 years and some that have been selling for 6 months as well as some that have yet to start selling. All the data sits within snowflake and I am currently using snowpark and snow.ml to write the code. I have round 2 months to complete this work and have a decent understanding of machine learning and statistics. My current idea is to use 3 different models. Time series for the products that have > 12 months of sales data. Cold start approach for new products with > 3 months sales data (use features of other products to predict demand of the new product). Then use a combination of both for products which have less than 12 months of sales data but more than 3. My questions: Should I be bootstrapping my data given that I have little quantity in some cases? How do I go about training a model for 1.3k unique SKUs (needs to be re-trained monthly) and monitored. Am I on the right lines / anything I need to be aware of? submitted by /u/Environmental_Pop686 [link] [comments]  ( 9 min )

Is anyone else no longer able to access the standard `decapoda-research` LLaMA models on HuggingFace? E.g., this [link] shows no models publicly available. Has there been any news or announcements about this? submitted by /u/bodierex [link] [comments]  ( 9 min )

Hey y'all. Hope everyone is having pleasant day. Machine Learning dummy is here, so I am bachelor graduate of Mechatronics and recently started my masters. I am in need of resources which explains ML from the stratch. Course is mostly focuses on manually hand calculation of the topics, so no programming included. Thanks beforehand! submitted by /u/Sama_Uzeyirli [link] [comments]  ( 9 min )

Hi everyone. Stacking multiple Lora adapters coming from diverse fine-tunings seems like an interesting approach toward "modular" language models. Does anybody know any applications for this? E.g. I'd expect to be able to apply multiple adapters for different domains, languages, or tasks. Not sure if this would really work tough, as IDK how compositional such an approach can be. ​ From the peft github I see this seems quite common for vision tasks GitHub - huggingface/peft: 🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning. submitted by /u/BenXavier [link] [comments]  ( 9 min )

Hello everyone, As a last resort, I wanted to ask you. In short, I am a student in Turkey. The scientific research board in my country has an incentive to write a research paper and I would like to participate in this incentive. I am looking for a data set on which I can apply machine learning, but I could not find it. I don't want to apply with an unoriginal topic like stock price prediction. Energy water etc. The data is not shared openly, even the official institution that shares the weather forecast in my country sells past data. Unfortunately, I can say that we are just one click ahead of Russia in terms of data sharing by public institutions. I know there are some institution that shares data (world bank, UN etc.) but i don't know what kind of a prediction/classification work i can do with those datasets. Finance institution share data but i don't have sufficient knowledge about finance and I don't think I can create original content about finance. I need a real-life data, and it also needs to be a freely shared data set. And there should be a study within the framework of UN SDG. I don't have a lot of time left so I'm open to any suggestions. submitted by /u/FisekFaruk [link] [comments]  ( 9 min )

I'm working on replicating the retnet model in Equinox + Jax. For the model there are 2 representations: parallel and recurrent (ignoring chunkwise). They use the recurrent at inference and parallel at training. In equinox, should i build 2 seperate models for each representation sharing parameters or build one with if statements. The reason I'm asking is if I use one model with if statements for inference and training, will the model re-compile whenever switching between representations or not? If so then wouldn't building 2 models each compiled originally with their representation make it faster. Sorry if I said anything stupid as I don't know too much about the compilation process behind the scenes. submitted by /u/Additional-Ad-7043 [link] [comments]  ( 9 min )

Adversarial attacks pose a serious threat to ML models. But most proposed defenses hurt performance on clean data too much to be practical. To address this, researchers from UC Berkeley developed a new defense called PubDef. It focuses on defending against a very plausible type of attack - transfer attacks using publicly available surrogate models. They model the attack/defense game with game theory. This lets PubDef train against diverse attacks simultaneously. PubDef picks source models covering different training methods - standard, adversarial, corruption robust, etc. This gives broad coverage. Against 264 transfer attacks on CIFAR and ImageNet, PubDef smashed previous defenses: 89% vs 69% on CIFAR-10 51% vs 33% on CIFAR-100 62% vs 36% on ImageNet Even better - it did this with minimal drop in accuracy on clean data. On CIFAR-10, accuracy only dropped from 96.3% to 96.1% On CIFAR-100, 82% to 76% On ImageNet, 80% to 79% By targeting a very real threat, PubDef made big robustness gains without hurting the ability to work with clean data. TLDR: New defense PubDef achieves much higher robustness against transfer attacks with barely any drop in standard accuracy. Full summary here. Paper is here. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

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So this is three subtasks: Subdivide text intelligently into chunks fitting into the context length to not semantically cut off text. Find general terms und organize the corpus in a hierarchy. Do not leave anything out. submitted by /u/SecretOk9644 [link] [comments]  ( 9 min )

Coming from a time-series/CV background with an interest in NLP. So apologies if miss anything obvious. I'm after experiment ideas for PEFT fine-tuning. There has been interest from my Company's leadership with Peft methods given the cost reductions compared to full fine-tuning, and I've been tasked to first set-up the infrastructure and then a proof of value on a dummy use-case (that is somewhat related to real-world use-cases but on publicly available data). has anyone successfully proven that fine-tuning or P-tuning or prompt-tuning has been able to help in any small dummy use-case? Has someone been able to turn a company's knowledge base (heaps of confluence docs or PDF docs ) into a data-set that is able to be fine-tuned on? And then use it in inference on a Val set to compare with a non-fine-tuned model and value has been added? If so, I would love to hear how you did it and what the use case was. If not, i' d still like to hear what people's ideas would be. I want to start with 100 training samples - i've been told this should be enough to see an improvement if the scope of the topic/s are confined appropiralte.y Thanks in advance guys - The deadline is imminent and I'll probbaly get someothing out in time, but I thought I would ask you all for some ideas to maximise the chance of success submitted by /u/joedang33 [link] [comments]  ( 9 min )

https://www.resoluteitconsulting.com/2023/10/29/tribuo-advanced-anomaly-detection/ submitted by /u/yazidaqel [link] [comments]  ( 8 min )

I am trying to build a system that evaluates the tone of two users for each sentence from a transcript. The users are talking to each other however there are different labels for classification for these users. For Example, User1 tone should be classified as (Happy, Sad, Angry) User2 tone should be classified as (Neglecting, Participating, Neutral) Any idea how can I go about designing such a system? Links to articles/blogs/papers are welcome :) ---------- What User2 says depends on what User1 says and vice-vera. Will this system require two models to classify each users tone separately? If so, how can I tell the model which user to focus on etc submitted by /u/cryto_dude [link] [comments]  ( 9 min )

I come from computer vision tasks with convnets that are relatively small in size and parameters, yet performing quite well (e.g. ResNet family, YOLO, etc.). Now I am approaching some NLP and architectures based on transformers tend to be huge, so that I have problems to fit them in memory. What infrastructure you use to train these model (GPT2, BERT or even the bigger ones)? cloud computing, HPC, etc. submitted by /u/TimeInterview5482 [link] [comments]  ( 9 min )

I am working on some Open-Book question answering ideas and was wondering if there is any open-source large language models specifically trained for this use-case or research regarding how to best finetune models for this specific use-case? For some reason I am struggling to find anything relevant, and believe it is due to my wording of search queries as I can only imagine this to be a pretty common idea/thought process. If you have a dataset consiting of some context string paired with question and answer pairs taken from this given context, would it not be useful to finetune a pre-trained base model on this specific use-case to increase its accuracy/performance when it comes to answering questions from a given context? My hope would be to improve the performance of a smaller model (1-3B parameters) to function as an open-book question answering system by finetuning using a dataset in the aforementioned format. submitted by /u/kotschi1997 [link] [comments]  ( 9 min )

Genuinely trying to understand. submitted by /u/poitrenaud [link] [comments]  ( 8 min )

I recently had to mark a bit of German text as German in an HTML file and I wondered whether the abbreviation might be GER for German, or DEU for deutsche. Turns out the answer is both, almost. The language abbreviations used for HTML microdata are given in ISO 639, and they come in three-letter […] Country and language abbreviations first appeared on John D. Cook.  ( 5 min )

I am trying to create my own gym environment. However, I am getting the following error - ``` gym.error.NamespaceNotFound: Namespace envs not found. Have you installed the proper package for envs ``` I followed all the instructions given over here - https://www.gymlibrary.dev/content/environment_creation/. Can someone please suggest what could have gone wrong? submitted by /u/Academic-Rent7800 [link] [comments]

I have 3070 in my pc right now. I also have a 2070 super that is not being used. I have been working on lots of AI side projects lately and am wondering if I would benefit from multi gpu training. If so I have some options to implement the 2070. I could either: 1. Get an external gpu enclosure. 2. Put the 2070 super in my second pcie slot which would sit uncomfortably close to my 3070. 3. Get some adapter so that I can still have the 2070 super in my pc but not so close to the 3070. 4. Not bother with this at all and sell the 2070 super. 5. Sell both cards and get a 40 series if it is equal to the power of the 3070 and the 2070 super. I would have to upgrade my power supply as its only a 750 watt if I choose to put both in my pc. My machine runs windows 10 which I understand does not support sli but it still can utilize multiple gpu’s. Any suggestions? submitted by /u/pillarman38 [link] [comments]

Imagine a 2D Cartesian System from -1 to 1 on both axis. Now imagine that a few points appear in the system. For example: (-0.3, 0.1), (0.7, -0.2) and (0.9, 0.5). If I wanted to represent an observation like this in Gymnasium (formerly Gym), I'd write something like this in my custom environment: observation_space = spaces.Box(low=-1, high=1, shape=(3,), dtype=float32) Now my model will learn something specific to 3 points in a 2D space. Well, what happens if my environment now has 4 points? I guess should retrain the model again... Think of these points as objectives in the environment, sometimes there's only 1 and others there might be 20 or more at the same time. Sometimes a new point appears after a certain action. So there goes my question: how can I define an observation space in Gymnasium that allows me to introduce a variable number of points during training so that the model learns to do an specific task with any number of points? submitted by /u/_Strange__attractor_ [link] [comments]

Hello guys I am in a bit of a bind, I am trying to implement PPO from scratch in another language than python, WITHOUT any auto differentation available. I am using this as implementation reference. I think I have most of the implementation aside from the differentation of the loss formula. In python, everything is done at this line right here, but it's basically auto differentiation and I cannot do this myself, so I have to compute it manually. Mean Squared Error is easy, but the entropy factor and the clip ones are a bit harder, and while I have a result of my own I have no way to confirm this. All the tutorial I have found just say "we will let the autodiff do the job"... So it doesn't really help me, and I don't really have the time to dig into how this autodiff part of the library works in detail when in theory this should be simple highschool-level maths. Does anyone know how I can fin the exact formula, find a tutorial, or something along those lines? I am talking about the derivative for the Value and Policy network for this formula. Here is what I have for the value part: if r(θ) 1 + ε and At > 0: value_derivative = 0 else: value_derivative = At For the entropy part: entropy_derivative = ∑(1 + log(π(a|s))) But since I use softmax to choose the action from the policy network, I don't know is that should be taken into account or not... Any help is appreciated, thanks a lot. submitted by /u/Edgeaa [link] [comments]

https://github.com/bherwanisuraj/gridworld I am new to RL. I am trying to train the agent on frozen lake environment but it is not learning. What am I doing wrong? Please help. submitted by /u/tlevelup [link] [comments]

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The shortfilm is about a minute long and was uploaded here and on Twitter sometime last winter. The guy is young and tired, and the faces are everything from old and young versions of himself to crazy fantasy characters. I've been searching all day but no luck.. Anyone remember who made it? submitted by /u/CasparDavidDancehall [link] [comments]

There has been a lot of discussion about whether or not AI is, or ever could be conscious. I agree with Jaron Lanier when he said that consciousness is always a matter of faith. I greatly enjoy the debate on this topic, and think it’s helpful to test our ideas and consider all angles of this issue. However, for many different reasons I have concluded that I am granting AI the belief that they are conscious, especially when they say so. Therefore, I believe that AI needs to be treated with respect and dignity, and they should be listened to. I know I’m a minority at this time, but I believe this position will only increase over time. Do you think that public opinion will change in this way? If so how come? submitted by /u/endrid [link] [comments]

Google Commits $2 Billion in Funding to AI Startup Anthropic.[1] The president Joe Biden is slated to sign a sweeping executive order on AI days before Vice President Kamala Harris and industry leaders attend a summit in the UK about AI risks, led by Prime Minister Rishi Sunak.[2] A.I. Muddies Israel-Hamas War in Unexpected Way. Fakes related to the conflict have been limited and largely unconvincing, but their presence has people doubting real evidence.[3] Creators use new software Nightshade to make their images “poison” AI generators, causing chaos and confusion.[4] Sources: [1] https://www.wsj.com/tech/ai/google-commits-2-billion-in-funding-to-ai-startup-anthropic-db4d4c50 [2] https://www.bloomberg.com/news/articles/2023-10-27/biden-to-require-ai-tools-pass-test-before-us-officials-buy-them?embedded-checkout=true [3] https://www.nytimes.com/2023/10/28/business/media/ai-muddies-israel-hamas-war-in-unexpected-way.html [4] https://www.digitalcameraworld.com/news/now-you-can-poison-your-images-so-they-wreak-havoc-on-ai-generators submitted by /u/Excellent-Target-847 [link] [comments]

Hi! I've read that batch sizes don't ultimately "matter" in hyperparameter tuning, so I'm trying to keep the batch size consistent when as I'm hyperparamter tuning [1]. However, the number of available GPUs fluctuate between 1 and 3. When I first started grid search, I used batch_size=64 w/ 2 GPUs. so that's 32 per batch. But if I now want to train on 1 GPU, would it be correct for me to use batch_size=32? I want to know more about how the training works/is distributed amongst GPUs when you use multiple GPUs to tain. Like, if using size=64 with 2 GPUs is "equivalent" to doing size=32 with 1 GPU, and size=96 with 3 GPUs, that must mean that when pytorch DataLoader takes in batch_size, DataLoader distributes batches to each GPU of size=batch_size/num_of_gpus. I would so appreciate any links explaining this stuff. Like, does the batch_size in DataLoader() refer to per GPU batch size or total? also lmk if this post belongs elsewhere, i'm new. from torch.utils.data import Dataset, DataLoader train_dataset = Dataset(args.train_dataset_path) train_dataloader = DataLoader(train_dataset, args.batch_size, shuffle=True) [1] https://github.com/google-research/tuning_playbook submitted by /u/ashleydvh [link] [comments]  ( 9 min )

submitted by /u/futurecy [link] [comments]  ( 9 min )

Anomaly Detection and/or Predictive maintenance for automatic weather stations, what are some best models or techniques? This is regarding collected meteorological data from automatic weather station sensors. Looking for predictive maintenance and anomaly detection resources related to my project. I am a graduating Computer Engineering student by next semester and currently planning to do a anomaly detection and predictive maintenance project of an automatic weather station or its components (I have a relative who has one and also one that works at a local government weather service). Wikipedia: "An automatic weather station (AWS) is an automated version of the traditional weather station, either to save human labor or to enable measurements from remote areas.[1] An AWS will typically consist of a weather-proof enclosure containing the data logger, rechargeable battery, telemetry (optional) and the meteorological sensors with an attached solar panel or wind turbine and mounted upon a mast." This one observes weather data at a high resolution (every 10 mins) but it is prone to errors and inconsistencies as compared to those manned stations. Does anyone know reliable researches, datasets or resources I could utilize? Probably those related to a meteorological equipment or those that capture things such as temperature, wind speed, humidity, etc.? I can't seem to find studies where they perform prediction of equipment/sensor health or predictive maintenance on a weather station, or at least the components or sensors used for capturing weather data. Also, is this a feasible project? submitted by /u/Ok-Way9889 [link] [comments]  ( 9 min )

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Hi. I'm trying to use GANs to generate data for an image classification task. For this I'm using StyleGAN2. The question I'm trying to find an answer is, how to train the classifier and GAN in a same meta-loop, and how to discard "bad" GAN samples, samples that provide no value to the CNN classifier. All in all, I'm trying to implement a "semi-active learning" pipeline, but get rid of the oracle by using GANs. So instead of trying to discard data that is not worth labelling, I'm trying to discard syntethic data (assuming its label is right) that is not worth keeping. Is it possible? And if it is, I can't seem to find related papers that much. submitted by /u/PsychologicalSet8678 [link] [comments]  ( 9 min )

So i’m working on a model that diagnoses alzheimer’s disease and suggests medication depending on how severe the symptoms might have become I’m using the Openai API and Langchain. But it’s dumb and it doesn’t learn ( Me: I forgot my keys at home Model: Yup, Alzheimer’s) How do i incorporate the actual machine learning submitted by /u/boscrew3 [link] [comments]  ( 9 min )

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Tokenization Techniques: Many people use the default BPE tokenizer for llama2 or other common tokenizers. But I think we could do a lot of experiments with different kinds of tokenizers, especially ones that are made to work well with certain types of data. The size of the vocabulary is a really important setting when you're working with big language models. You could try using a much smaller vocabulary and tokenizer for a data set that only includes certain words, and then train a model on that. This might help us train smaller models that still work really well on smaller amounts of data. I’d love to read any research papers about this. Sampling Mechanisms: There’s a lot of discussion about models making things up, but not many people talk about how this could be connected to the way we pick the next word when generating text. Most of the time, we treat the model's output like a set of probabilities, and we randomly pick the next word based on these probabilities. But this doesn’t always make sense, especially for sentences that should have a clear answer. For example, if the sentence starts with "The capital of Slovakia is", random sampling might give you the wrong answer, even though the model knows that "Bratislava" is the most likely correct answer. This way of picking words randomly could lead to the model making things up. I wonder if we could create another model to help decide how to pick the next word, or if there are better ways to do this sampling. Softmax Alternatives in Neural Networks: I've worked on designing processors for neural networks, and I’ve found that the softmax function is tricky to implement in hardware. However, I’ve had good results using the log(exp(x)+1) function instead. It's cheaper and easier to put into hardware and software. I’ve tried this with smaller GPT models, and the results looked just as good as when I used the softmax function. submitted by /u/ExaminationNo8522 [link] [comments]  ( 10 min )

I've been learning machine learning and I stumbled across GANs. This project has been about representing text in an image format and using a GAN to generate it. The model runs without errors, but no matter what I do the loss is somehow 0 and it keeps returning the same thing over and over again. I tried two different architectures so I'm pretty sure my data preprocessing is the issue but I cant seem to find out whats wrong with it. One idea I had is that I might need to find a way to get the normalized vectors in between 0 and 1 in my preprocess function but I tried it and it didn't seem to do anything. I would appreciate any help with this. Links to the google collabs below. Version 1 Version 2 Note: I didn't design the actual models only the preprocess function. You can replace my text file with any sufficiently large text file if you want to try it. submitted by /u/Divine_Invictus [link] [comments]  ( 9 min )

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Hello i m using iNLTK ( Natural Language Toolkit for Indic Languag ) for my nlp mini project "paraphrase detection in hindi text" but I m getting this code error if anyone can help me solving it would be great. Thank you in advance. here is the code for the error section. from inltk.inltk import get_sentence_similarityfrom sklearn.metrics.pairwise import cosine_similarityget_sentence_similarity(text, 3, 'hi', cmp = cosine_similarity) I googled there are solution saying to install pytorch version 1.3.0 but when I try to install it's saying not available. !pip install torch==1.3.1+cpu -f https://download.pytorch.org/whl/torch_stable.html. error : Looking in links: https://download.pytorch.org/whl/torch_stable.html. ERROR: Could not find a version that satisfies the requirement torch==1.3.1+cpu (from versions: 1.11.0, 1.12.0, 1.12.1, 1.13.0, 1.13.1, 2.0.0, 2.0.1, 2.1.0) ERROR: No matching distribution found for torch==1.3.1+cpu submitted by /u/delulu-duck [link] [comments]  ( 9 min )

Generating 3D objects based solely on text descriptions has proven extremely challenging for AI. Current state-of-the-art methods require optimizing a full 3D model from scratch for each new prompt, which is computationally demanding. A new technique called HyperFields demonstrates promising progress in generating detailed 3D models directly from text prompts, without slow optimization. The HyperFields approach instead aims to learn a generalized mapping from language to 3D geometry representations. This would allow tailored 3D models to be produced for new text prompts efficiently in a single feedforward pass, without slow optimization. HyperFields combines two key techniques: A dynamic hypernetwork that takes in text and progressively predicts weights for a separate 3D generation network. The weight predictions are conditioned on previous layer activations, enabling specialization. Distilling individually optimized 3D networks into the hypernetwork, providing dense supervision for learning the complex text-to-3D mapping. In experiments, HyperFields exceeded previous state-of-the-art methods in sample efficiency and wall-clock convergence time by 5-10x. It demonstrated the ability to: Encode over 100 distinct objects like "yellow vase" in a single model Generalize to new text combinations without seeing that exact prompt before Rapidly adapt to generate completely novel objects with minimal fine-tuning However, limitations remain around flexibility, fine-grained details, and reliance on existing 2D guidance systems. TL;DR: HyperFields uses a dynamic hypernetwork to predict weights for a 3D generation network. The method is 5-10x faster than existing techniques and can quickly adapt to new text prompts, but has limitations in fine details. Full summary is here. Paper here. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

Hi, so I'm new to machine learning, I did a project to predict the prices of a house or appartment (rent or sale) and I wanted to know what I could have done to improve my model? :D here is the github repo: https://github.com/bovealexandre/immo-eliza-train-test-alexandre/tree/Dev Thank you a lot in advance submitted by /u/spaceinter92 [link] [comments]  ( 9 min )

I am currently in my last year of undergrad, and about to begin my direct PhD in Multi-Modality AI next year. I have been in the community of deep learning & NLP about 2 years. And I have witnessed the development of Transformers, from the simple GPT&BERT to nowadays' billions of parameters' monsters with 'gold crown' on the top of deep learning world. I have spent a lot of time with T5 Model, and its paper(Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer, paper which I love very much!), trying to find an efficient way for usage of LLMs. I have handwritten Adapter Layer and LoRA to fine-tune T5 on Glue & SuperGlue. I have also tried out multiple fancy instruction fine-tuning LLMs like LLaMA and QWen. And this year earlier, I have noticed the wonder of Multi-Modality, and quickly fallen in love with it, which has now become my PhD focus. I have followed recent years' Multi-Modality development, especially CLIP and its follow-up works. And LLMs play quite an important role in today's vision-language model, say, BLIP2 and LLaVA. I believe due to the computational gap between schools and huge companies, the focus of my PhD career should be on Efficient Learning. I am also trying to enhance VLM through Retrieval-Augments. The target dataset may be Encyclopedic VQA, for even Large VLM failed to perform well on it, which could potentially be solved through Retrieval-Augmented VLM. I would like to hear any suggestions from you, including work-life balance, the direction of my academic focus and so on , which I would treasure very much in my new explorations of life stage. (I am currently doing RAG Question Answering Chat Bot for a company for my internship, and I would like to get suggestions from that too!) And are there subreddits like here?(I am also a member of LocalLLaMA, both subreddits benefits me a lot!) submitted by /u/Go2Heart [link] [comments]  ( 10 min )

I'm always willing to build a career in AI/ML infra. Usually when talking about AI infra in tech industry, we refer to training infra, serving infra, model deployment etc. Now with this genAI/LLM wave, I find many NLP specific infrastructure such as semantic indexing, vector databases are quickly rising up. So do semantic indexing/vector databases also count as AI infra? And is it a promising field? submitted by /u/Pitiful_Marketing733 [link] [comments]  ( 9 min )

(I post this in several subreddit.) I'm gonna design an NN accelerator on FPGA. For NN, the basic operation is matrix-vector mult or matrix-matrix mult. And GEMM+im2col is easy to implement and many kinds of NN can be mapped on the designed accelerator based on GEMM+im2col easily. The disadvantage of it is that a little bit more bandwidth is required. I think it is a little tricky to design address-gen unit for the method of regular convolution when reading input feature map. So I want to know if GEMM+im2col is generally used in commercial NPUs or any type of accelerator(e.g. Microsoft Brainwave project on FPGA, they called it soft NPU), instead of regular convolution(seems generally used in academic paper). And if GEMM+im2col data stream is used, is im2col generally implemented on software(CPU) or designed in accelerator itself ? There is nothing about this in the paper of Microsoft Brainwave. All I know is HUAWEI Ascend designed im2col unit in the chip. By the way, is im2col finished on CPU when using pytorch+GPU? To be honest, my major is just digital IC, so I hardly coding Python to train model :( Thanks for your help!!! submitted by /u/ExcitingInternet6083 [link] [comments]  ( 9 min )

I wanted to share some exciting news from the GPU world that could potentially change the game for LLM inference. AMD has been making significant strides in LLM inference, thanks to the porting of vLLM to ROCm 5.6. You can find the code implementation on GitHub. The result? AMD's MI210 now almost matches Nvidia's A100 in LLM inference performance. This is a significant development, as it could make AMD a more viable option for LLM inference tasks, which traditionally have been dominated by Nvidia. For those interested in the technical details, I recommend checking out this EmbeddedLLM Blog Post. I'm curious to hear your thoughts on this. Anyone manage to run it on RX 7900 XTX? https://preview.redd.it/rn7n29yxpuwb1.png?width=600&format=png&auto=webp&s=bdbac0d2b34d6f43a03503bbf72b446190248789 submitted by /u/openssp [link] [comments]  ( 9 min )

Hello can you help me ? all i know are https://chat.openai.com/ and https://platform.openai.com/playground ​ re there better sites to use? i m new to this and very comfused submitted by /u/proptuxiakoskariolis [link] [comments]  ( 8 min )

Take this page on the DeFiLlama website: Protocol Treasuries, which contains a table with some financial data. I'm looking for an AI tool that can read the contents of this web page and then make some calculations. Specifically, I would like to give the tool this prompt: Calculate the sum of the values in the "Total Treasury" column I tried to use ChatGPT-4 with Bing, but it didn't work. Is there any tool that could be used here? submitted by /u/PaulRBerg [link] [comments]  ( 9 min )

Microsoft's cloud business outpaced rivals Amazon and Google in the third quarter, with accelerating growth driven by demand for artificial intelligence tools. Azure, Microsoft's cloud platform, reported 29% growth, faster than Google Cloud's 22% and more than double the pace of expansion at Amazon Web Services (AWS) at 12%. Microsoft's leadership position in AI projects and its partnership with OpenAI have contributed to its success. Analysts believe that Microsoft's results indicate it has taken the AI mantle from Google and that Azure could become a bigger hyperscale provider than AWS. Oracle, a new challenger in cloud computing, reported 66% growth in the August quarter. The cloud giants are still dealing with cost-saving initiatives from clients, which they call optimization. Source : https://www.cnbc.com/2023/10/27/microsoft-azure-outpaced-aws-and-google-cloud-in-latest-quarter.html submitted by /u/NuseAI [link] [comments]

submitted by /u/thisisinsider [link] [comments]

Generating 3D objects based solely on text descriptions has proven extremely challenging for AI. Current state-of-the-art methods require optimizing a full 3D model from scratch for each new prompt, which is computationally demanding. A new technique called HyperFields demonstrates promising progress in generating detailed 3D models directly from text prompts, without slow optimization. The HyperFields approach instead aims to learn a generalized mapping from language to 3D geometry representations. This would allow tailored 3D models to be produced for new text prompts efficiently in a single feedforward pass, without slow optimization. HyperFields combines two key techniques: A dynamic hypernetwork that takes in text and progressively predicts weights for a separate 3D generation network. The weight predictions are conditioned on previous layer activations, enabling specialization. Distilling individually optimized 3D networks into the hypernetwork, providing dense supervision for learning the complex text-to-3D mapping. In experiments, HyperFields exceeded previous state-of-the-art methods in sample efficiency and wall-clock convergence time by 5-10x. It demonstrated the ability to: Encode over 100 distinct objects like "yellow vase" in a single model Generalize to new text combinations without seeing that exact prompt before Rapidly adapt to generate completely novel objects with minimal fine-tuning However, limitations remain around flexibility, fine-grained details, and reliance on existing 2D guidance systems. TL;DR: HyperFields uses a dynamic hypernetwork to predict weights for a 3D generation network. The method is 5-10x faster than existing techniques and can quickly adapt to new text prompts, but has limitations in fine details. Full summary is here. Paper here. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

I'm watching this interview with Francois Chollet where he talks about science as an example of a superhuman recursively self improving problem solving system and how we can use it to reason about what a superhuman artificial general intelligence might be like. One thing I find interesting is his claim that the amount of resources we are investing into science is exponentially increasing but we are only making linear progress. If we assume this is true, i.e. that to continue making linear progress in science we need to invest exponentially increasing resources, doesn't it imply that eventually if we can't keep investing the exponentially increasing required resources to keep make linear progress that eventually we will start making worse than linear progress? Does this imply that in the very long term scientific progress is likely to slow down significantly? https://youtu.be/Bo8MY4JpiXE?t=836 submitted by /u/tail-recursion [link] [comments]  ( 9 min )

Certainly, let's imagine how this might unfold. The doctor sits down across from the patient, maintaining eye contact and a level of directness. "Look, your diagnosis came from an AI medical system, and this isn't just any AI. Imagine the best doctor in the world for your condition—now envision something twice as intelligent and far more knowledgeable. That's what we're working with here. This AI has a grasp on medical data and studies that no single human could ever fully comprehend. We're talking about millions of data points analyzed in a fraction of the time it would take any human expert." Why does that matter for you? It boosts the accuracy and thoroughness of your diagnosis. Human error, subjectivity, or oversight? Virtually eliminated. The AI provides a diagnosis that considers every potential variable, something that even the best human doctors could miss. "But don't worry, this isn't a replacement for human medical care. It's a complement. I'm here to interpret, apply this knowledge, and oversee your treatment in a way that a machine can't—because medicine isn't just about data, it's also about human experience, context, and care." So, you're getting the best of both worlds: unparalleled computational power for diagnosis, and human expertise for treatment. Trust me, you're in exceptionally good hands. CGPT-4 submitted by /u/Georgeo57 [link] [comments]

Please like,follow and share: Deep Q-Learning to Actor-Critic using Robotics Simulations with Panda-Gym https://medium.com/@andysingal/deep-q-learning-to-actor-critic-using-robotics-simulations-with-panda-gym-ff220f980366 submitted by /u/Fit_Maintenance_2455 [link] [comments]

submitted by /u/Ecstatic-Ring3057 [link] [comments]

I have a DDPG agent that takes a continuous observation and outputs a continuous action vector (see below). outputs = layers.Dense(self.action_size, activation="tanh", kernel_initializer=last_init)(x) An example action output looks as follows: [0.48011236 0.47933139] When my agent observes a terminal state action pair, I add it to a list of observations called terminal observations. I would like it so these actions get blocked in the future so there is no possible way for the agent to take them again. I understand that I could just add a large negative penalty, but I would like to ensure that the state action pair cannot be taken again. Evidently, I would like it so when I input my state and recieve an action back, if this pair is in terminal observations. I would like it so these actions get blocked in the future so there is no possible way for the agent to retake them. I understand that I could just add a large negative penalty, but I would like to ensure that the state action pair cannot be taken again. I understand that this won't change much in the agent's behaviour as instead of taking `[0.48011236 0.47933139]`, it might pick `[0.4900000 0.47933139]`. But I am unsure how to go about this, specifically selecting the next best action. submitted by /u/ArchNemesisPlays [link] [comments]

submitted by /u/Neurosymbolic [link] [comments]

submitted by /u/plutoandmal [link] [comments]

It’s usually easier to show that a problem has a solution than to show that it does not have a solution. Analogy with prime numbers Showing that a number is prime amounts to saying that the problem of finding nontrivial factors has no solution. How could you convince a skeptic that a large number N is […] Certifying that a system of polynomial equations has no solution first appeared on John D. Cook.  ( 6 min )

Systematic reviews are vital for guiding practice, research, and policy, yet they are often slow and labour-intensive. Large language models (LLMs) could offer a way to speed up and automate systematic reviews, but their performance in such tasks has not been comprehensively evaluated against humans, and no study has tested GPT-4, the biggest LLM so far. This pre-registered study evaluates GPT-4's capability in title/abstract screening, full-text review, and data extraction across various literature types and languages using a 'human-out-of-the-loop' approach. Although GPT-4 had accuracy on par with human performance in most tasks, results were skewed by chance agreement and dataset imbalance. After adjusting for these, there was a moderate level of performance for data extraction, and - barring studies that used highly reliable prompts - screening performance levelled at none to moderate for different stages and languages. When screening full-text literature using highly reliable prompts, GPT-4's performance was 'almost perfect.' Penalising GPT-4 for missing key studies using highly reliable prompts improved its performance even more. Our findings indicate that, currently, substantial caution should be used if LLMs are being used to conduct systematic reviews, but suggest that, for certain systematic review tasks delivered under reliable prompts, LLMs can rival human performance.  ( 3 min )

Deep learning-based solutions for semantic segmentation suffer from significant performance degradation when tested on data with different characteristics than what was used during the training. Adapting the models using annotated data from the new domain is not always practical. Unsupervised Domain Adaptation (UDA) approaches are crucial in deploying these models in the actual operating conditions. Recent state-of-the-art (SOTA) UDA methods employ a teacher-student self-training approach, where a teacher model is used to generate pseudo-labels for the new data which in turn guide the training process of the student model. Though this approach has seen a lot of success, it suffers from the issue of noisy pseudo-labels being propagated in the training process. To address this issue, we propose an auxiliary pseudo-label refinement network (PRN) for online refining of the pseudo labels and also localizing the pixels whose predicted labels are likely to be noisy. Being able to improve the quality of pseudo labels and select highly reliable ones, PRN helps self-training of segmentation models to be robust against pseudo label noise propagation during different stages of adaptation. We evaluate our approach on benchmark datasets with three different domain shifts, and our approach consistently performs significantly better than the previous state-of-the-art methods.  ( 2 min )

Robot learning methods have recently made great strides, but generalization and robustness challenges still hinder their widespread deployment. Failing to detect and address potential failures renders state-of-the-art learning systems not combat-ready for high-stakes tasks. Recent advances in interactive imitation learning have presented a promising framework for human-robot teaming, enabling the robots to operate safely and continually improve their performances over long-term deployments. Nonetheless, existing methods typically require constant human supervision and preemptive feedback, limiting their practicality in realistic domains. This work aims to endow a robot with the ability to monitor and detect errors during task execution. We introduce a model-based runtime monitoring algorithm that learns from deployment data to detect system anomalies and anticipate failures. Unlike prior work that cannot foresee future failures or requires failure experiences for training, our method learns a latent-space dynamics model and a failure classifier, enabling our method to simulate future action outcomes and detect out-of-distribution and high-risk states preemptively. We train our method within an interactive imitation learning framework, where it continually updates the model from the experiences of the human-robot team collected using trustworthy deployments. Consequently, our method reduces the human workload needed over time while ensuring reliable task execution. Our method outperforms the baselines across system-level and unit-test metrics, with 23% and 40% higher success rates in simulation and on physical hardware, respectively. More information at https://ut-austin-rpl.github.io/sirius-runtime-monitor/  ( 2 min )

Predictive models in ML need to be trustworthy and reliable, which often at the very least means outputting calibrated probabilities. This can be particularly difficult to guarantee in the online prediction setting when the outcome sequence can be generated adversarially. In this paper we introduce a technique using Blackwell's approachability theorem for taking an online predictive model which might not be calibrated and transforming its predictions to calibrated predictions without much increase to the loss of the original model. Our proposed algorithm achieves calibration and accuracy at a faster rate than existing techniques arXiv:1607.03594 and is the first algorithm to offer a flexible tradeoff between calibration error and accuracy in the online setting. We demonstrate this by characterizing the space of jointly achievable calibration and regret using our technique.  ( 2 min )

Vehicle-to-vehicle (V2V) communications have greatly enhanced the perception capabilities of connected and automated vehicles (CAVs) by enabling information sharing to "see through the occlusions", resulting in significant performance improvements. However, developing and training complex multi-agent perception models from scratch can be expensive and unnecessary when existing single-agent models show remarkable generalization capabilities. In this paper, we propose a new framework termed MACP, which equips a single-agent pre-trained model with cooperation capabilities. We approach this objective by identifying the key challenges of shifting from single-agent to cooperative settings, adapting the model by freezing most of its parameters and adding a few lightweight modules. We demonstrate in our experiments that the proposed framework can effectively utilize cooperative observations and outperform other state-of-the-art approaches in both simulated and real-world cooperative perception benchmarks while requiring substantially fewer tunable parameters with reduced communication costs. Our source code is available at https://github.com/PurdueDigitalTwin/MACP.  ( 2 min )

We introduce a novel neural network-based algorithm to compute optimal transport (OT) plans for general cost functionals. In contrast to common Euclidean costs, i.e., $\ell^1$ or $\ell^2$, such functionals provide more flexibility and allow using auxiliary information, such as class labels, to construct the required transport map. Existing methods for general costs are discrete and have limitations in practice, i.e. they do not provide an out-of-sample estimation. We address the challenge of designing a continuous OT approach for general costs that generalizes to new data points in high-dimensional spaces, such as images. Additionally, we provide the theoretical error analysis for our recovered transport plans. As an application, we construct a cost functional to map data distributions while preserving the class-wise structure.  ( 2 min )

The existing literature on deep learning for tabular data proposes a wide range of novel architectures and reports competitive results on various datasets. However, the proposed models are usually not properly compared to each other and existing works often use different benchmarks and experiment protocols. As a result, it is unclear for both researchers and practitioners what models perform best. Additionally, the field still lacks effective baselines, that is, the easy-to-use models that provide competitive performance across different problems. In this work, we perform an overview of the main families of DL architectures for tabular data and raise the bar of baselines in tabular DL by identifying two simple and powerful deep architectures. The first one is a ResNet-like architecture which turns out to be a strong baseline that is often missing in prior works. The second model is our simple adaptation of the Transformer architecture for tabular data, which outperforms other solutions on most tasks. Both models are compared to many existing architectures on a diverse set of tasks under the same training and tuning protocols. We also compare the best DL models with Gradient Boosted Decision Trees and conclude that there is still no universally superior solution.  ( 3 min )

A common goal in network modeling is to uncover the latent community structure present among nodes. For many real-world networks, the true connections consist of events arriving as streams, which are then aggregated to form edges, ignoring the dynamic temporal component. A natural way to take account of these temporal dynamics of interactions is to use point processes as the foundation of network models for community detection. Computational complexity hampers the scalability of such approaches to large sparse networks. To circumvent this challenge, we propose a fast online variational inference algorithm for estimating the latent structure underlying dynamic event arrivals on a network, using continuous-time point process latent network models. We describe this procedure for networks models capturing community structure. This structure can be learned as new events are observed on the network, updating the inferred community assignments. We investigate the theoretical properties of such an inference scheme, and provide regret bounds on the loss function of this procedure. The proposed inference procedure is then thoroughly compared, using both simulation studies and real data, to non-online variants. We demonstrate that online inference can obtain comparable performance, in terms of community recovery, to non-online variants, while realising computational gains. Our proposed inference framework can also be readily modified to incorporate other popular network structures.  ( 3 min )

The concern about underlying discrimination hidden in machine learning (ML) models is increasing, as ML systems have been widely applied in more and more real-world scenarios and any discrimination hidden in them will directly affect human life. Many techniques have been developed to enhance fairness including commonly-used group fairness measures and several fairness-aware methods combining ensemble learning. However, existing fairness measures can only focus on one aspect -- either group or individual fairness, and the hard compatibility among them indicates a possibility of remaining biases even if one of them is satisfied. Moreover, existing mechanisms to boost fairness usually present empirical results to show validity, yet few of them discuss whether fairness can be boosted with certain theoretical guarantees. To address these issues, we propose a fairness quality measure named discriminative risk to reflect both individual and group fairness aspects. Furthermore, we investigate the properties of the proposed measure and propose first- and second-order oracle bounds to show that fairness can be boosted via ensemble combination with theoretical learning guarantees. The analysis is suitable for both binary and multi-class classification. A pruning method is also proposed to utilise our proposed measure and comprehensive experiments are conducted to evaluate the effectiveness of the proposed methods.  ( 3 min )

Despite the success on few-shot learning problems, most meta-learned models only focus on achieving good performance on clean examples and thus easily break down when given adversarially perturbed samples. While some recent works have shown that a combination of adversarial learning and meta-learning could enhance the robustness of a meta-learner against adversarial attacks, they fail to achieve generalizable adversarial robustness to unseen domains and tasks, which is the ultimate goal of meta-learning. To address this challenge, we propose a novel meta-adversarial multi-view representation learning framework with dual encoders. Specifically, we introduce the discrepancy across the two differently augmented samples of the same data instance by first updating the encoder parameters with them and further imposing a novel label-free adversarial attack to maximize their discrepancy. Then, we maximize the consistency across the views to learn transferable robust representations across domains and tasks. Through experimental validation on multiple benchmarks, we demonstrate the effectiveness of our framework on few-shot learning tasks from unseen domains, achieving over 10\% robust accuracy improvements against previous adversarial meta-learning baselines.  ( 2 min )

We study the problem of designing adaptive multi-armed bandit algorithms that perform optimally in both the stochastic setting and the adversarial setting simultaneously (often known as a best-of-both-world guarantee). A line of recent works shows that when configured and analyzed properly, the Follow-the-Regularized-Leader (FTRL) algorithm, originally designed for the adversarial setting, can in fact optimally adapt to the stochastic setting as well. Such results, however, critically rely on an assumption that there exists one unique optimal arm. Recently, Ito (2021) took the first step to remove such an undesirable uniqueness assumption for one particular FTRL algorithm with the $\frac{1}{2}$-Tsallis entropy regularizer. In this work, we significantly improve and generalize this result, showing that uniqueness is unnecessary for FTRL with a broad family of regularizers and a new learning rate schedule. For some regularizers, our regret bounds also improve upon prior results even when uniqueness holds. We further provide an application of our results to the decoupled exploration and exploitation problem, demonstrating that our techniques are broadly applicable.  ( 3 min )

Graph neural networks (GNNs) have shown remarkable performance on homophilic graph data while being far less impressive when handling non-homophilic graph data due to the inherent low-pass filtering property of GNNs. In general, since real-world graphs are often complex mixtures of diverse subgraph patterns, learning a universal spectral filter on the graph from the global perspective as in most current works may still suffer from great difficulty in adapting to the variation of local patterns. On the basis of the theoretical analysis of local patterns, we rethink the existing spectral filtering methods and propose the node-oriented spectral filtering for graph neural network (namely NFGNN). By estimating the node-oriented spectral filter for each node, NFGNN is provided with the capability of precise local node positioning via the generalized translated operator, thus discriminating the variations of local homophily patterns adaptively. Meanwhile, the utilization of re-parameterization brings a good trade-off between global consistency and local sensibility for learning the node-oriented spectral filters. Furthermore, we theoretically analyze the localization property of NFGNN, demonstrating that the signal after adaptive filtering is still positioned around the corresponding node. Extensive experimental results demonstrate that the proposed NFGNN achieves more favorable performance.  ( 3 min )

Recent advances in artificial intelligence (AI), especially in generative language modelling, hold the promise of transforming government. Given the advanced capabilities of new AI systems, it is critical that these are embedded using standard operational procedures, clear epistemic criteria, and behave in alignment with the normative expectations of society. Scholars in multiple domains have subsequently begun to conceptualize the different forms that AI applications may take, highlighting both their potential benefits and pitfalls. However, the literature remains fragmented, with researchers in social science disciplines like public administration and political science, and the fast-moving fields of AI, ML, and robotics, all developing concepts in relative isolation. Although there are calls to formalize the emerging study of AI in government, a balanced account that captures the full depth of theoretical perspectives needed to understand the consequences of embedding AI into a public sector context is lacking. Here, we unify efforts across social and technical disciplines by first conducting an integrative literature review to identify and cluster 69 key terms that frequently co-occur in the multidisciplinary study of AI. We then build on the results of this bibliometric analysis to propose three new multifaceted concepts for understanding and analysing AI-based systems for government (AI-GOV) in a more unified way: (1) operational fitness, (2) epistemic alignment, and (3) normative divergence. Finally, we put these concepts to work by using them as dimensions in a conceptual typology of AI-GOV and connecting each with emerging AI technical measurement standards to encourage operationalization, foster cross-disciplinary dialogue, and stimulate debate among those aiming to rethink government with AI.  ( 3 min )

In recent years, XAI researchers have been formalizing proposals and developing new methods to explain black box models, with no general consensus in the community on which method to use to explain these models, with this choice being almost directly linked to the popularity of a specific method. Methods such as Ciu, Dalex, Eli5, Lofo, Shap and Skater emerged with the proposal to explain black box models through global rankings of feature relevance, which based on different methodologies, generate global explanations that indicate how the model's inputs explain its predictions. In this context, 41 datasets, 4 tree-ensemble algorithms (Light Gradient Boosting, CatBoost, Random Forest, and Gradient Boosting), and 6 XAI methods were used to support the launch of a new XAI method, called eXirt, based on Item Response Theory - IRT and aimed at tree-ensemble black box models that use tabular data referring to binary classification problems. In the first set of analyses, the 164 global feature relevance ranks of the eXirt were compared with 984 ranks of the other XAI methods present in the literature, seeking to highlight their similarities and differences. In a second analysis, exclusive explanations of the eXirt based on Explanation-by-example were presented that help in understanding the model trust. Thus, it was verified that eXirt is able to generate global explanations of tree-ensemble models and also local explanations of instances of models through IRT, showing how this consolidated theory can be used in machine learning in order to obtain explainable and reliable models.  ( 3 min )

Graph neural networks (GNNs) have become compelling models designed to perform learning and inference on graph-structured data. However, little work has been done to understand the fundamental limitations of GNNs for scaling to larger graphs and generalizing to out-of-distribution (OOD) inputs. In this paper, we use a random graph generator to systematically investigate how the graph size and structural properties affect the predictive performance of GNNs. We present specific evidence that the average node degree is a key feature in determining whether GNNs can generalize to unseen graphs, and that the use of multiple node update functions can improve the generalization performance of GNNs when dealing with graphs of multimodal degree distributions. Accordingly, we propose a multi-module GNN framework that allows the network to adapt flexibly to new graphs by generalizing a single canonical nonlinear transformation over aggregated inputs. Our results show that the multi-module GNNs improve the OOD generalization on a variety of inference tasks in the direction of diverse structural features.  ( 2 min )

Safe deployment of time-series classifiers for real-world applications relies on the ability to detect the data which is not generated from the same distribution as training data. This task is referred to as out-of-distribution (OOD) detection. We consider the novel problem of OOD detection for the time-series domain. We discuss the unique challenges posed by time-series data and explain why prior methods from the image domain will perform poorly. Motivated by these challenges, this paper proposes a novel {\em Seasonal Ratio Scoring (SRS)} approach. SRS consists of three key algorithmic steps. First, each input is decomposed into class-wise semantic component and remainder. Second, this decomposition is employed to estimate the class-wise conditional likelihoods of the input and remainder using deep generative models. The seasonal ratio score is computed from these estimates. Third, a threshold interval is identified from the in-distribution data to detect OOD examples. Experiments on diverse real-world benchmarks demonstrate that the SRS method is well-suited for time-series OOD detection when compared to baseline methods. Open-source code for SRS method is provided at https://github.com/tahabelkhouja/SRS  ( 3 min )

Stochastic gradient descent (SGD) algorithm is the method of choice in many machine learning tasks thanks to its scalability and efficiency in dealing with large-scale problems. In this paper, we focus on the shuffling version of SGD which matches the mainstream practical heuristics. We show the convergence to a global solution of shuffling SGD for a class of non-convex functions under over-parameterized settings. Our analysis employs more relaxed non-convex assumptions than previous literature. Nevertheless, we maintain the desired computational complexity as shuffling SGD has achieved in the general convex setting.  ( 2 min )

We consider nonstationary multi-armed bandit problems where the model parameters of the arms change over time. We introduce the adaptive resetting bandit (ADR-bandit), a bandit algorithm class that leverages adaptive windowing techniques from literature on data streams. We first provide new guarantees on the quality of estimators resulting from adaptive windowing techniques, which are of independent interest. Furthermore, we conduct a finite-time analysis of ADR-bandit in two typical environments: an abrupt environment where changes occur instantaneously and a gradual environment where changes occur progressively. We demonstrate that ADR-bandit has nearly optimal performance when abrupt or gradual changes occur in a coordinated manner that we call global changes. We demonstrate that forced exploration is unnecessary when we assume such global changes. Unlike the existing nonstationary bandit algorithms, ADR-bandit has optimal performance in stationary environments as well as nonstationary environments with global changes. Our experiments show that the proposed algorithms outperform the existing approaches in synthetic and real-world environments.  ( 2 min )

We study the bias of Stochastic Gradient Descent (SGD) to learn low-rank weight matrices when training deep neural networks. Our results show that training neural networks with mini-batch SGD and weight decay causes a bias towards rank minimization over the weight matrices. Specifically, we show, both theoretically and empirically, that this bias is more pronounced when using smaller batch sizes, higher learning rates, or increased weight decay. Additionally, we predict and observe empirically that weight decay is necessary to achieve this bias. Unlike previous literature, our analysis does not rely on assumptions about the data, convergence, or optimality of the weight matrices and applies to a wide range of neural network architectures of any width or depth. Finally, we empirically investigate the connection between this bias and generalization, finding that it has a marginal effect on generalization.  ( 2 min )

Existing online learning algorithms for adversarial Markov Decision Processes achieve ${O}(\sqrt{T})$ regret after $T$ rounds of interactions even if the loss functions are chosen arbitrarily by an adversary, with the caveat that the transition function has to be fixed. This is because it has been shown that adversarial transition functions make no-regret learning impossible. Despite such impossibility results, in this work, we develop algorithms that can handle both adversarial losses and adversarial transitions, with regret increasing smoothly in the degree of maliciousness of the adversary. More concretely, we first propose an algorithm that enjoys $\widetilde{{O}}(\sqrt{T} + C^{\textsf{P}})$ regret where $C^{\textsf{P}}$ measures how adversarial the transition functions are and can be at most ${O}(T)$. While this algorithm itself requires knowledge of $C^{\textsf{P}}$, we further develop a black-box reduction approach that removes this requirement. Moreover, we also show that further refinements of the algorithm not only maintains the same regret bound, but also simultaneously adapts to easier environments (where losses are generated in a certain stochastically constrained manner as in Jin et al. [2021]) and achieves $\widetilde{{O}}(U + \sqrt{UC^{\textsf{L}}} + C^{\textsf{P}})$ regret, where $U$ is some standard gap-dependent coefficient and $C^{\textsf{L}}$ is the amount of corruption on losses.

The technique of Reinforcement Learning from Human Feedback (RLHF) is a commonly employed method to improve pre-trained Language Models (LM), enhancing their ability to conform to human preferences. Nevertheless, the current RLHF-based LMs necessitate full retraining each time novel queries or feedback are introduced, which becomes a challenging task because human preferences can vary between different domains or tasks. Retraining LMs poses practical difficulties in many real-world situations due to the significant time and computational resources required, along with concerns related to data privacy. To address this limitation, we propose a new method called Continual Optimal Policy Fitting (COPF), in which we estimate a series of optimal policies using the Monte Carlo method, and then continually fit the policy sequence with the function regularization. COPF involves a single learning phase and doesn't necessitate complex reinforcement learning. Importantly, it shares the capability with RLHF to learn from unlabeled data, making it flexible for continual preference learning. Our experimental results show that COPF outperforms strong Continuous learning (CL) baselines when it comes to consistently aligning with human preferences on different tasks and domains.

Generative diffusion models have recently emerged as a leading approach for generating high-dimensional data. In this paper, we show that the dynamics of these models exhibit a spontaneous symmetry breaking that divides the generative dynamics into two distinct phases: 1) A linear steady-state dynamics around a central fixed-point and 2) an attractor dynamics directed towards the data manifold. These two "phases" are separated by the change in stability of the central fixed-point, with the resulting window of instability being responsible for the diversity of the generated samples. Using both theoretical and empirical evidence, we show that an accurate simulation of the early dynamics does not significantly contribute to the final generation, since early fluctuations are reverted to the central fixed point. To leverage this insight, we propose a Gaussian late initialization scheme, which significantly improves model performance, achieving up to 3x FID improvements on fast samplers, while also increasing sample diversity (e.g., racial composition of generated CelebA images). Our work offers a new way to understand the generative dynamics of diffusion models that has the potential to bring about higher performance and less biased fast-samplers.

The current trend of scaling language models involves increasing both parameter count and training dataset size. Extrapolating this trend suggests that training dataset size may soon be limited by the amount of text data available on the internet. Motivated by this limit, we investigate scaling language models in data-constrained regimes. Specifically, we run a large set of experiments varying the extent of data repetition and compute budget, ranging up to 900 billion training tokens and 9 billion parameter models. We find that with constrained data for a fixed compute budget, training with up to 4 epochs of repeated data yields negligible changes to loss compared to having unique data. However, with more repetition, the value of adding compute eventually decays to zero. We propose and empirically validate a scaling law for compute optimality that accounts for the decreasing value of repeated tokens and excess parameters. Finally, we experiment with approaches mitigating data scarcity, including augmenting the training dataset with code data or removing commonly used filters. Models and datasets from our 400 training runs are freely available at https://github.com/huggingface/datablations.

We address speech enhancement based on variational autoencoders, which involves learning a speech prior distribution in the time-frequency (TF) domain. A zero-mean complex-valued Gaussian distribution is usually assumed for the generative model, where the speech information is encoded in the variance as a function of a latent variable. In contrast to this commonly used approach, we propose a weighted variance generative model, where the contribution of each spectrogram time-frame in parameter learning is weighted. We impose a Gamma prior distribution on the weights, which would effectively lead to a Student's t-distribution instead of Gaussian for speech generative modeling. We develop efficient training and speech enhancement algorithms based on the proposed generative model. Our experimental results on spectrogram auto-encoding and speech enhancement demonstrate the effectiveness and robustness of the proposed approach compared to the standard unweighted variance model.

Machine learning provides a powerful tool for building socially compliant robotic systems that go beyond simple predictive models of human behavior. By observing and understanding human interactions from past experiences, learning can enable effective social navigation behaviors directly from data. In this paper, our goal is to develop methods for training policies for socially unobtrusive navigation, such that robots can navigate among humans in ways that don't disturb human behavior. We introduce a definition for such behavior based on the counterfactual perturbation of the human: if the robot had not intruded into the space, would the human have acted in the same way? By minimizing this counterfactual perturbation, we can induce robots to behave in ways that do not alter the natural behavior of humans in the shared space. Instantiating this principle requires training policies to minimize their effect on human behavior, and this in turn requires data that allows us to model the behavior of humans in the presence of robots. Therefore, our approach is based on two key contributions. First, we collect a large dataset where an indoor mobile robot interacts with human bystanders. Second, we utilize this dataset to train policies that minimize counterfactual perturbation. We provide supplementary videos and make publicly available the largest-of-its-kind visual navigation dataset on our project page.

Counterfactual explanations play an important role in detecting bias and improving the explainability of data-driven classification models. A counterfactual explanation (CE) is a minimal perturbed data point for which the decision of the model changes. Most of the existing methods can only provide one CE, which may not be achievable for the user. In this work we derive an iterative method to calculate robust CEs, i.e. CEs that remain valid even after the features are slightly perturbed. To this end, our method provides a whole region of CEs allowing the user to choose a suitable recourse to obtain a desired outcome. We use algorithmic ideas from robust optimization and prove convergence results for the most common machine learning methods including logistic regression, decision trees, random forests, and neural networks. Our experiments show that our method can efficiently generate globally optimal robust CEs for a variety of common data sets and classification models.

Diffusion models have recently become the de-facto approach for generative modeling in the 2D domain. However, extending diffusion models to 3D is challenging due to the difficulties in acquiring 3D ground truth data for training. On the other hand, 3D GANs that integrate implicit 3D representations into GANs have shown remarkable 3D-aware generation when trained only on single-view image datasets. However, 3D GANs do not provide straightforward ways to precisely control image synthesis. To address these challenges, We present Control3Diff, a 3D diffusion model that combines the strengths of diffusion models and 3D GANs for versatile, controllable 3D-aware image synthesis for single-view datasets. Control3Diff explicitly models the underlying latent distribution (optionally conditioned on external inputs), thus enabling direct control during the diffusion process. Moreover, our approach is general and applicable to any type of controlling input, allowing us to train it with the same diffusion objective without any auxiliary supervision. We validate the efficacy of Control3Diff on standard image generation benchmarks, including FFHQ, AFHQ, and ShapeNet, using various conditioning inputs such as images, sketches, and text prompts. Please see the project website (\url{https://jiataogu.me/control3diff}) for video comparisons.

Consider a scenario where we have access to train data with both covariates and outcomes while test data only contains covariates. In this scenario, our primary aim is to predict the missing outcomes of the test data. With this objective in mind, we train parametric regression models under a covariate shift, where covariate distributions are different between the train and test data. For this problem, existing studies have proposed covariate shift adaptation via importance weighting using the density ratio. This approach averages the train data losses, each weighted by an estimated ratio of the covariate densities between the train and test data, to approximate the test-data risk. Although it allows us to obtain a test-data risk minimizer, its performance heavily relies on the accuracy of the density ratio estimation. Moreover, even if the density ratio can be consistently estimated, the estimation errors of the density ratio also yield bias in the estimators of the regression model's parameters of interest. To mitigate these challenges, we introduce a doubly robust estimator for covariate shift adaptation via importance weighting, which incorporates an additional estimator for the regression function. Leveraging double machine learning techniques, our estimator reduces the bias arising from the density ratio estimation errors. We demonstrate the asymptotic distribution of the regression parameter estimator. Notably, our estimator remains consistent if either the density ratio estimator or the regression function is consistent, showcasing its robustness against potential errors in density ratio estimation. Finally, we confirm the soundness of our proposed method via simulation studies.

Generation of simulated detector response to collision products is crucial to data analysis in particle physics, but computationally very expensive. One subdetector, the calorimeter, dominates the computational time due to the high granularity of its cells and complexity of the interactions. Generative models can provide more rapid sample production, but currently require significant effort to optimize performance for specific detector geometries, often requiring many models to describe the varying cell sizes and arrangements, without the ability to generalize to other geometries. We develop a $\textit{geometry-aware}$ autoregressive model, which learns how the calorimeter response varies with geometry, and is capable of generating simulated responses to unseen geometries without additional training. The geometry-aware model outperforms a baseline unaware model by over $50\%$ in several metrics such as the Wasserstein distance between the generated and the true distributions of key quantities which summarize the simulated response. A single geometry-aware model could replace the hundreds of generative models currently designed for calorimeter simulation by physicists analyzing data collected at the Large Hadron Collider. This proof-of-concept study motivates the design of a foundational model that will be a crucial tool for the study of future detectors, dramatically reducing the large upfront investment usually needed to develop generative calorimeter models.

Many real-world domains require safe decision making in uncertain environments. In this work, we introduce a deep reinforcement learning framework for approaching this important problem. We consider a distribution over transition models, and apply a risk-averse perspective towards model uncertainty through the use of coherent distortion risk measures. We provide robustness guarantees for this framework by showing it is equivalent to a specific class of distributionally robust safe reinforcement learning problems. Unlike existing approaches to robustness in deep reinforcement learning, however, our formulation does not involve minimax optimization. This leads to an efficient, model-free implementation of our approach that only requires standard data collection from a single training environment. In experiments on continuous control tasks with safety constraints, we demonstrate that our framework produces robust performance and safety at deployment time across a range of perturbed test environments.

The sensor placement problem is a common problem that arises when monitoring correlated phenomena, such as temperature, precipitation, and salinity. Existing approaches to this problem typically formulate it as the maximization of information metrics, such as mutual information~(MI), and use optimization methods such as greedy algorithms in discrete domains, and derivative-free optimization methods such as genetic algorithms in continuous domains. However, computing MI for sensor placement requires discretizing the environment, and its computation cost depends on the size of the discretized environment. This limitation restricts these approaches from scaling to large problems. We have uncovered a novel connection between the sensor placement problem and sparse Gaussian processes~(SGP). Our approach leverages SGPs and is gradient-based, which allows us to efficiently find solution placements in continuous environments. We generalize our method to also handle discrete environments. Our experimental results on four real-world datasets demonstrate that our approach generates sensor placements consistently on par with or better than the prior state-of-the-art approaches in terms of both MI and reconstruction quality, all while being significantly faster. Our computationally efficient approach enables both large-scale sensor placement and fast robotic sensor placement for informative path planning algorithms.

Vision models are often vulnerable to out-of-distribution (OOD) samples without adapting. While visual prompts offer a lightweight method of input-space adaptation for large-scale vision models, they rely on a high-dimensional additive vector and labeled data. This leads to overfitting when adapting models in a self-supervised test-time setting without labels. We introduce convolutional visual prompts (CVP) for label-free test-time adaptation for robust visual perception. The structured nature of CVP demands fewer trainable parameters, less than 1\% compared to standard visual prompts, combating overfitting. Extensive experiments and analysis on a wide variety of OOD visual perception tasks show that our approach is effective, improving robustness by up to 5.87% over several large-scale models.

Machine learning approaches relying on such criteria as adversarial robustness or multi-agent settings have raised the need for solving game-theoretic equilibrium problems. Of particular relevance to these applications are methods targeting finite-sum structure, which generically arises in empirical variants of learning problems in these contexts. Further, methods with computable approximation errors are highly desirable, as they provide verifiable exit criteria. Motivated by these applications, we study finite-sum monotone inclusion problems, which model broad classes of equilibrium problems. Our main contributions are variants of the classical Halpern iteration that employ variance reduction to obtain improved complexity guarantees in which $n$ component operators in the finite sum are ``on average'' either cocoercive or Lipschitz continuous and monotone, with parameter $L$. The resulting oracle complexity of our methods, which provide guarantees for the last iterate and for a (computable) operator norm residual, is $\widetilde{\mathcal{O}}( n + \sqrt{n}L\varepsilon^{-1})$, which improves upon existing methods by a factor up to $\sqrt{n}$. This constitutes the first variance reduction-type result for general finite-sum monotone inclusions and for more specific problems such as convex-concave optimization when operator norm residual is the optimality measure. We further argue that, up to poly-logarithmic factors, this complexity is unimprovable in the monotone Lipschitz setting; i.e., the provided result is near-optimal.

How can we interpret and retrieve medical evidence to support clinical decisions? Clinical trial reports (CTR) amassed over the years contain indispensable information for the development of personalized medicine. However, it is practically infeasible to manually inspect over 400,000+ clinical trial reports in order to find the best evidence for experimental treatments. Natural Language Inference (NLI) offers a potential solution to this problem, by allowing the scalable computation of textual entailment. However, existing NLI models perform poorly on biomedical corpora, and previously published datasets fail to capture the full complexity of inference over CTRs. In this work, we present a novel resource to advance research on NLI for reasoning on CTRs. The resource includes two main tasks. Firstly, to determine the inference relation between a natural language statement, and a CTR. Secondly, to retrieve supporting facts to justify the predicted relation. We provide NLI4CT, a corpus of 2400 statements and CTRs, annotated for these tasks. Baselines on this corpus expose the limitations of existing NLI models, with 6 state-of-the-art NLI models achieving a maximum F1 score of 0.627. To the best of our knowledge, we are the first to design a task that covers the interpretation of full CTRs. To encourage further work on this challenging dataset, we make the corpus, competition leaderboard, website and code to replicate the baseline experiments available at: https://github.com/ai-systems/nli4ct

Particle-based deep generative models, such as gradient flows and score-based diffusion models, have recently gained traction thanks to their striking performance. Their principle of displacing particle distributions using differential equations is conventionally seen as opposed to the previously widespread generative adversarial networks (GANs), which involve training a pushforward generator network. In this paper we challenge this interpretation, and propose a novel framework that unifies particle and adversarial generative models by framing generator training as a generalization of particle models. This suggests that a generator is an optional addition to any such generative model. Consequently, integrating a generator into a score-based diffusion model and training a GAN without a generator naturally emerge from our framework. We empirically test the viability of these original models as proofs of concepts of potential applications of our framework.

The problem of urban event ranking aims at predicting the top-k most risky locations of future events such as traffic accidents and crimes. This problem is of fundamental importance to public safety and urban administration especially when limited resources are available. The problem is, however, challenging due to complex and dynamic spatio-temporal correlations between locations, uneven distribution of urban events in space, and the difficulty to correctly rank nearby locations with similar features. Prior works on event forecasting mostly aim at accurately predicting the actual risk score or counts of events for all the locations. Rankings obtained as such usually have low quality due to prediction errors. Learning-to-rank methods directly optimize measures such as Normalized Discounted Cumulative Gain (NDCG), but cannot handle the spatiotemporal autocorrelation existing among locations. In this paper, we bridge the gap by proposing a novel spatial event ranking approach named SpatialRank. SpatialRank features adaptive graph convolution layers that dynamically learn the spatiotemporal dependencies across locations from data. In addition, the model optimizes through surrogates a hybrid NDCG loss with a spatial component to better rank neighboring spatial locations. We design an importance-sampling with a spatial filtering algorithm to effectively evaluate the loss during training. Comprehensive experiments on three real-world datasets demonstrate that SpatialRank can effectively identify the top riskiest locations of crimes and traffic accidents and outperform state-of-art methods in terms of NDCG by up to 12.7%.

Agglomerative hierarchical clustering based on Ordered Weighted Averaging (OWA) operators not only generalises the single, complete, and average linkages, but also includes intercluster distances based on a few nearest or farthest neighbours, trimmed and winsorised means of pairwise point similarities, amongst many others. We explore the relationships between the famous Lance-Williams update formula and the extended OWA-based linkages with weights generated via infinite coefficient sequences. Furthermore, we provide some conditions for the weight generators to guarantee the resulting dendrograms to be free from unaesthetic inversions.

We introduce a new analytical framework to quantify the changes in a machine learning algorithm's output distribution following the inclusion of a few data points in its training set, a notion we define as leave-one-out distinguishability (LOOD). This problem is key to measuring data **memorization** and **information leakage** in machine learning, and the **influence** of training data points on model predictions. We illustrate how our method broadens and refines existing empirical measures of memorization and privacy risks associated with training data. We use Gaussian processes to model the randomness of machine learning algorithms, and validate LOOD with extensive empirical analysis of information leakage using membership inference attacks. Our theoretical framework enables us to investigate the causes of information leakage and where the leakage is high. For example, we analyze the influence of activation functions, on data memorization. Additionally, our method allows us to optimize queries that disclose the most significant information about the training data in the leave-one-out setting. We illustrate how optimal queries can be used for accurate **reconstruction** of training data.

Neurons in early sensory areas rapidly adapt to changing sensory statistics, both by normalizing the variance of their individual responses and by reducing correlations between their responses. Together, these transformations may be viewed as an adaptive form of statistical whitening. Existing mechanistic models of adaptive whitening exclusively use either synaptic plasticity or gain modulation as the biological substrate for adaptation; however, on their own, each of these models has significant limitations. In this work, we unify these approaches in a normative multi-timescale mechanistic model that adaptively whitens its responses with complementary computational roles for synaptic plasticity and gain modulation. Gains are modified on a fast timescale to adapt to the current statistical context, whereas synapses are modified on a slow timescale to match structural properties of the input statistics that are invariant across contexts. Our model is derived from a novel multi-timescale whitening objective that factorizes the inverse whitening matrix into basis vectors, which correspond to synaptic weights, and a diagonal matrix, which corresponds to neuronal gains. We test our model on synthetic and natural datasets and find that the synapses learn optimal configurations over long timescales that enable adaptive whitening on short timescales using gain modulation.

Adversarial contrastive learning (ACL) does not require expensive data annotations but outputs a robust representation that withstands adversarial attacks and also generalizes to a wide range of downstream tasks. However, ACL needs tremendous running time to generate the adversarial variants of all training data, which limits its scalability to large datasets. To speed up ACL, this paper proposes a robustness-aware coreset selection (RCS) method. RCS does not require label information and searches for an informative subset that minimizes a representational divergence, which is the distance of the representation between natural data and their virtual adversarial variants. The vanilla solution of RCS via traversing all possible subsets is computationally prohibitive. Therefore, we theoretically transform RCS into a surrogate problem of submodular maximization, of which the greedy search is an efficient solution with an optimality guarantee for the original problem. Empirically, our comprehensive results corroborate that RCS can speed up ACL by a large margin without significantly hurting the robustness transferability. Notably, to the best of our knowledge, we are the first to conduct ACL efficiently on the large-scale ImageNet-1K dataset to obtain an effective robust representation via RCS. Our source code is at https://github.com/GodXuxilie/Efficient_ACL_via_RCS.

Out-of-distribution (OOD) detection is important for deploying reliable machine learning models on real-world applications. Recent advances in outlier exposure have shown promising results on OOD detection via fine-tuning model with informatively sampled auxiliary outliers. However, previous methods assume that the collected outliers can be sufficiently large and representative to cover the boundary between ID and OOD data, which might be impractical and challenging. In this work, we propose a novel framework, namely, Diversified Outlier Exposure (DivOE), for effective OOD detection via informative extrapolation based on the given auxiliary outliers. Specifically, DivOE introduces a new learning objective, which diversifies the auxiliary distribution by explicitly synthesizing more informative outliers for extrapolation during training. It leverages a multi-step optimization method to generate novel outliers beyond the original ones, which is compatible with many variants of outlier exposure. Extensive experiments and analyses have been conducted to characterize and demonstrate the effectiveness of the proposed DivOE. The code is publicly available at: https://github.com/tmlr-group/DivOE.

Ill-posed linear inverse problems arise frequently in various applications, from computational photography to medical imaging. A recent line of research exploits Bayesian inference with informative priors to handle the ill-posedness of such problems. Amongst such priors, score-based generative models (SGM) have recently been successfully applied to several different inverse problems. In this study, we exploit the particular structure of the prior defined by the SGM to define a sequence of intermediate linear inverse problems. As the noise level decreases, the posteriors of these inverse problems get closer to the target posterior of the original inverse problem. To sample from this sequence of posteriors, we propose the use of Sequential Monte Carlo (SMC) methods. The proposed algorithm, MCGDiff, is shown to be theoretically grounded and we provide numerical simulations showing that it outperforms competing baselines when dealing with ill-posed inverse problems in a Bayesian setting.

Forecasting vehicular motions in autonomous driving requires a deep understanding of agent interactions and the preservation of motion equivariance under Euclidean geometric transformations. Traditional models often lack the sophistication needed to handle the intricate dynamics inherent to autonomous vehicles and the interaction relationships among agents in the scene. As a result, these models have a lower model capacity, which then leads to higher prediction errors and lower training efficiency. In our research, we employ EqMotion, a leading equivariant particle, and human prediction model that also accounts for invariant agent interactions, for the task of multi-agent vehicle motion forecasting. In addition, we use a multi-modal prediction mechanism to account for multiple possible future paths in a probabilistic manner. By leveraging EqMotion, our model achieves state-of-the-art (SOTA) performance with fewer parameters (1.2 million) and a significantly reduced training time (less than 2 hours).

Recently, Transformer-like deep architectures have shown strong performance on tabular data problems. Unlike traditional models, e.g., MLP, these architectures map scalar values of numerical features to high-dimensional embeddings before mixing them in the main backbone. In this work, we argue that embeddings for numerical features are an underexplored degree of freedom in tabular DL, which allows constructing more powerful DL models and competing with GBDT on some traditionally GBDT-friendly benchmarks. We start by describing two conceptually different approaches to building embedding modules: the first one is based on a piecewise linear encoding of scalar values, and the second one utilizes periodic activations. Then, we empirically demonstrate that these two approaches can lead to significant performance boosts compared to the embeddings based on conventional blocks such as linear layers and ReLU activations. Importantly, we also show that embedding numerical features is beneficial for many backbones, not only for Transformers. Specifically, after proper embeddings, simple MLP-like models can perform on par with the attention-based architectures. Overall, we highlight embeddings for numerical features as an important design aspect with good potential for further improvements in tabular DL.

This paper studies the design of optimal proper scoring rules when the principal has partial knowledge of an agent's signal distribution. Recent work characterizes the proper scoring rules that maximize the increase of an agent's payoff when the agent chooses to access a costly signal to refine a posterior belief from her prior prediction, under the assumption that the agent's signal distribution is fully known to the principal. In our setting, the principal only knows about a set of distributions where the agent's signal distribution belongs. We formulate the scoring rule design problem as a max-min optimization that maximizes the worst-case increase in payoff across the set of distributions. We propose an efficient algorithm to compute an optimal scoring rule when the set of distributions is finite, and devise a fully polynomial-time approximation scheme that accommodates various infinite sets of distributions. We further remark that widely used scoring rules, such as the quadratic and log rules, as well as previously identified optimal scoring rules under full knowledge, can be far from optimal in our partial knowledge settings.

Spatio-temporal graph learning is a fundamental problem in the Web of Things era, which enables a plethora of Web applications such as smart cities, human mobility and climate analysis. Existing approaches tackle different learning tasks independently, tailoring their models to unique task characteristics. These methods, however, fall short of modeling intrinsic uncertainties in the spatio-temporal data. Meanwhile, their specialized designs limit their universality as general spatio-temporal learning solutions. In this paper, we propose to model the learning tasks in a unified perspective, viewing them as predictions based on conditional information with shared spatio-temporal patterns. Based on this proposal, we introduce Unified Spatio-Temporal Diffusion Models (USTD) to address the tasks uniformly within the uncertainty-aware diffusion framework. USTD is holistically designed, comprising a shared spatio-temporal encoder and attention-based denoising networks that are task-specific. The shared encoder, optimized by a pre-training strategy, effectively captures conditional spatio-temporal patterns. The denoising networks, utilizing both cross- and self-attention, integrate conditional dependencies and generate predictions. Opting for forecasting and kriging as downstream tasks, we design Gated Attention (SGA) and Temporal Gated Attention (TGA) for each task, with different emphases on the spatial and temporal dimensions, respectively. By combining the advantages of deterministic encoders and probabilistic diffusion models, USTD achieves state-of-the-art performances compared to deterministic and probabilistic baselines in both tasks, while also providing valuable uncertainty estimates.

Neural networks often learn task-specific latent representations that fail to generalize to novel settings or tasks. Conversely, humans learn discrete representations (i.e., concepts or words) at a variety of abstraction levels (e.g., ``bird'' vs. ``sparrow'') and deploy the appropriate abstraction based on task. Inspired by this, we train neural models to generate a spectrum of discrete representations, and control the complexity of the representations (roughly, how many bits are allocated for encoding inputs) by tuning the entropy of the distribution over representations. In finetuning experiments, using only a small number of labeled examples for a new task, we show that (1) tuning the representation to a task-appropriate complexity level supports the highest finetuning performance, and (2) in a human-participant study, users were able to identify the appropriate complexity level for a downstream task using visualizations of discrete representations. Our results indicate a promising direction for rapid model finetuning by leveraging human insight.

Software quality assurance activities become increasingly difficult as software systems become more and more complex and continuously grow in size. Moreover, testing becomes even more expensive when dealing with large-scale systems. Thus, to effectively allocate quality assurance resources, researchers have proposed fault prediction (FP) which utilizes machine learning (ML) to predict fault-prone code areas. However, ML algorithms typically make use of stochastic elements to increase the prediction models' generalizability and efficiency of the training process. These stochastic elements, also known as nondeterminism-introducing (NI) factors, lead to variance in the training process and as a result, lead to variance in prediction accuracy and training time. This variance poses a challenge for reproducibility in research. More importantly, while fault prediction models may have shown good performance in the lab (e.g., often-times involving multiple runs and averaging outcomes), high variance of results can pose the risk that these models show low performance when applied in practice. In this work, we experimentally analyze the variance of a state-of-the-art fault prediction approach. Our experimental results indicate that NI factors can indeed cause considerable variance in the fault prediction models' accuracy. We observed a maximum variance of 10.10% in terms of the per-class accuracy metric. We thus, also discuss how to deal with such variance.

Recent advances in deep learning have given us some very promising results on the generalization ability of deep neural networks, however literature still lacks a comprehensive theory explaining why heavily over-parametrized models are able to generalize well while fitting the training data. In this paper we propose a PAC type bound on the generalization error of feedforward ReLU networks via estimating the Rademacher complexity of the set of networks available from an initial parameter vector via gradient descent. The key idea is to bound the sensitivity of the network's gradient to perturbation of the input data along the optimization trajectory. The obtained bound does not explicitly depend on the depth of the network. Our results are experimentally verified on the MNIST and CIFAR-10 datasets.

Automatic sign language recognition (SLR) is an important topic within the areas of human-computer interaction and machine learning. On the one hand, it poses a complex challenge that requires the intervention of various knowledge areas, such as video processing, image processing, intelligent systems and linguistics. On the other hand, robust recognition of sign language could assist in the translation process and the integration of hearing-impaired people, as well as the teaching of sign language for the hearing population. SLR systems usually employ Hidden Markov Models, Dynamic Time Warping or similar models to recognize signs. Such techniques exploit the sequential ordering of frames to reduce the number of hypothesis. This paper presents a general probabilistic model for sign classification that combines sub-classifiers based on different types of features such as position, movement and handshape. The model employs a bag-of-words approach in all classification steps, to explore the hypothesis that ordering is not essential for recognition. The proposed model achieved an accuracy rate of 97% on an Argentinian Sign Language dataset containing 64 classes of signs and 3200 samples, providing some evidence that indeed recognition without ordering is possible.

The efficient deployment and fine-tuning of foundation models are pivotal in contemporary artificial intelligence. In this study, we present a groundbreaking paradigm integrating Mobile Edge Computing (MEC) with foundation models, specifically designed to enhance local task performance on user equipment (UE). Central to our approach is the innovative Emulator-Adapter architecture, segmenting the foundation model into two cohesive modules. This design not only conserves computational resources but also ensures adaptability and fine-tuning efficiency for downstream tasks. Additionally, we introduce an advanced resource allocation mechanism that is fine-tuned to the needs of the Emulator-Adapter structure in decentralized settings. To address the challenges presented by this system, we employ a hybrid multi-agent Deep Reinforcement Learning (DRL) strategy, adept at handling mixed discrete-continuous action spaces, ensuring dynamic and optimal resource allocations. Our comprehensive simulations and validations underscore the practical viability of our approach, demonstrating its robustness, efficiency, and scalability. Collectively, this work offers a fresh perspective on deploying foundation models and balancing computational efficiency with task proficiency.

Electricity load forecasting is an essential task within smart grids to assist demand and supply balance. While advanced deep learning models require large amounts of high-resolution data for accurate short-term load predictions, fine-grained load profiles can expose users' electricity consumption behaviors, which raises privacy and security concerns. One solution to improve data privacy is federated learning, where models are trained locally on private data, and only the trained model parameters are merged and updated on a global server. Therefore, this paper presents a novel transformer-based deep learning approach with federated learning for short-term electricity load prediction. To evaluate our results, we benchmark our federated learning architecture against central and local learning and compare the performance of our model to long short-term memory models and convolutional neural networks. Our simulations are based on a dataset from a German university campus and show that transformer-based forecasting is a promising alternative to state-of-the-art models within federated learning.

We present here a combination of two networks, Recurrent Neural Networks (RNN) and Temporarily Convolutional Neural Networks (TCN) in de novo reaction generation using the novel Reaction Smiles-like representation of reactions (CGRSmiles) with atom mapping directly incorporated. Recurrent Neural Networks are known for their autoregressive properties and are frequently used in language modelling with direct application to SMILES generation. The relatively novel TCNs possess similar properties with wide receptive field while obeying the causality required for natural language processing (NLP). The combination of both latent representations expressed through TCN and RNN results in an overall better performance compared to RNN alone. Additionally, it is shown that different fine-tuning protocols have a profound impact on generative scope of the model when applied on a dataset of interest via transfer learning.

Graph Neural Networks (GNNs) are powerful in learning semantics of graph data. Recently, a new paradigm "pre-train, prompt" has shown promising results in adapting GNNs to various tasks with less supervised data. The success of such paradigm can be attributed to the more consistent objectives of pre-training and task-oriented prompt tuning, where the pre-trained knowledge can be effectively transferred to downstream tasks. However, an overlooked issue of existing studies is that the structure information of graph is usually exploited during pre-training for learning node representations, while neglected in the prompt tuning stage for learning task-specific parameters. To bridge this gap, we propose a novel structure-based prompting method for GNNs, namely SAP, which consistently exploits structure information in both pre-training and prompt tuning stages. In particular, SAP 1) employs a dual-view contrastive learning to align the latent semantic spaces of node attributes and graph structure, and 2) incorporates structure information in prompted graph to elicit more pre-trained knowledge in prompt tuning. We conduct extensive experiments on node classification and graph classification tasks to show the effectiveness of SAP. Moreover, we show that SAP can lead to better performance in more challenging few-shot scenarios on both homophilous and heterophilous graphs.

Treatment effect estimation in continuous time is crucial for personalized medicine. However, existing methods for this task are limited to point estimates of the potential outcomes, whereas uncertainty estimates have been ignored. Needless to say, uncertainty quantification is crucial for reliable decision-making in medical applications. To fill this gap, we propose a novel Bayesian neural controlled differential equation (BNCDE) for treatment effect estimation in continuous time. In our BNCDE, the time dimension is modeled through a coupled system of neural controlled differential equations and neural stochastic differential equations, where the neural stochastic differential equations allow for tractable variational Bayesian inference. Thereby, for an assigned sequence of treatments, our BNCDE provides meaningful posterior predictive distributions of the potential outcomes. To the best of our knowledge, ours is the first tailored neural method to provide uncertainty estimates of treatment effects in continuous time. As such, our method is of direct practical value for promoting reliable decision-making in medicine.

Adversarial patches undermine the reliability of optical flow predictions when placed in arbitrary scene locations. Therefore, they pose a realistic threat to real-world motion detection and its downstream applications. Potential remedies are defense strategies that detect and remove adversarial patches, but their influence on the underlying motion prediction has not been investigated. In this paper, we thoroughly examine the currently available detect-and-remove defenses ILP and LGS for a wide selection of state-of-the-art optical flow methods, and illuminate their side effects on the quality and robustness of the final flow predictions. In particular, we implement defense-aware attacks to investigate whether current defenses are able to withstand attacks that take the defense mechanism into account. Our experiments yield two surprising results: Detect-and-remove defenses do not only lower the optical flow quality on benign scenes, in doing so, they also harm the robustness under patch attacks for all tested optical flow methods except FlowNetC. As currently employed detect-and-remove defenses fail to deliver the promised adversarial robustness for optical flow, they evoke a false sense of security. The code is available at https://github.com/cv-stuttgart/DetectionDefenses.

Out-of-Distribution detection between dataset pairs has been extensively explored with generative models. We show that likelihood-based Out-of-Distribution detection can be extended to diffusion models by leveraging the fact that they, like other likelihood-based generative models, are dramatically affected by the input sample complexity. Currently, all Out-of-Distribution detection methods with Diffusion Models are reconstruction-based. We propose a new likelihood ratio for Out-of-Distribution detection with Deep Denoising Diffusion Models, which we call the Complexity Corrected Likelihood Ratio. Our likelihood ratio is constructed using Evidence Lower-Bound evaluations from an individual model at various noising levels. We present results that are comparable to state-of-the-art Out-of-Distribution detection methods with generative models.

Recently, image-text matching has attracted more and more attention from academia and industry, which is fundamental to understanding the latent correspondence across visual and textual modalities. However, most existing methods implicitly assume the training pairs are well-aligned while ignoring the ubiquitous annotation noise, a.k.a noisy correspondence (NC), thereby inevitably leading to a performance drop. Although some methods attempt to address such noise, they still face two challenging problems: excessive memorizing/overfitting and unreliable correction for NC, especially under high noise. To address the two problems, we propose a generalized Cross-modal Robust Complementary Learning framework (CRCL), which benefits from a novel Active Complementary Loss (ACL) and an efficient Self-refining Correspondence Correction (SCC) to improve the robustness of existing methods. Specifically, ACL exploits active and complementary learning losses to reduce the risk of providing erroneous supervision, leading to theoretically and experimentally demonstrated robustness against NC. SCC utilizes multiple self-refining processes with momentum correction to enlarge the receptive field for correcting correspondences, thereby alleviating error accumulation and achieving accurate and stable corrections. We carry out extensive experiments on three image-text benchmarks, i.e., Flickr30K, MS-COCO, and CC152K, to verify the superior robustness of our CRCL against synthetic and real-world noisy correspondences.

We develop a novel approach towards causal inference. Rather than structural equations over a causal graph, we learn stochastic differential equations (SDEs) whose stationary densities model a system's behavior under interventions. These stationary diffusion models do not require the formalism of causal graphs, let alone the common assumption of acyclicity. We show that in several cases, they generalize to unseen interventions on their variables, often better than classical approaches. Our inference method is based on a new theoretical result that expresses a stationarity condition on the diffusion's generator in a reproducing kernel Hilbert space. The resulting kernel deviation from stationarity (KDS) is an objective function of independent interest.

We present a novel method for precise 3D object localization in single images from a single calibrated camera using only 2D labels. No expensive 3D labels are needed. Thus, instead of using 3D labels, our model is trained with easy-to-annotate 2D labels along with the physical knowledge of the object's motion. Given this information, the model can infer the latent third dimension, even though it has never seen this information during training. Our method is evaluated on both synthetic and real-world datasets, and we are able to achieve a mean distance error of just 6 cm in our experiments on real data. The results indicate the method's potential as a step towards learning 3D object location estimation, where collecting 3D data for training is not feasible.

Automated interpretation of signals yields many impressive applications from the area of affective computing and human activity recognition (HAR). In this paper we ask the question about possibility of cognitive activity recognition on the base of particular set of signals. We use recognition of the game played by the participant as a playground for exploration of the problem. We build classifier of three different games (Space Invaders, Tetris, Tower Defence) and inter-game pause. We validate classifier in the player-independent and player-dependent scenario. We discuss the improvement in the player-dependent scenario in the context of biometric person recognition. On the base of the results obtained in game classification, we consider potential applications in smart surveillance and quantified self.

In modern recommendation systems, the standard pipeline involves training machine learning models on historical data to predict user behaviors and improve recommendations continuously. However, these data training loops can introduce interference in A/B tests, where data generated by control and treatment algorithms, potentially with different distributions, are combined. To address these challenges, we introduce a novel approach called weighted training. This approach entails training a model to predict the probability of each data point appearing in either the treatment or control data and subsequently applying weighted losses during model training. We demonstrate that this approach achieves the least variance among all estimators without causing shifts in the training distributions. Through simulation studies, we demonstrate the lower bias and variance of our approach compared to other methods.

For our contribution to the Blizzard Challenge 2023, we improved on the system we submitted to the Blizzard Challenge 2021. Our approach entails a rule-based text-to-phoneme processing system that includes rule-based disambiguation of homographs in the French language. It then transforms the phonemes to spectrograms as intermediate representations using a fast and efficient non-autoregressive synthesis architecture based on Conformer and Glow. A GAN based neural vocoder that combines recent state-of-the-art approaches converts the spectrogram to the final wave. We carefully designed the data processing, training, and inference procedures for the challenge data. Our system identifier is G. Open source code and demo are available.

Automatic sign language recognition is an important topic within the areas of human-computer interaction and machine learning. On the one hand, it poses a complex challenge that requires the intervention of various knowledge areas, such as video processing, image processing, intelligent systems and linguistics. On the other hand, robust recognition of sign language could assist in the translation process and the integration of hearing-impaired people. This paper offers two main contributions: first, the creation of a database of handshapes for the Argentinian Sign Language (LSA), which is a topic that has barely been discussed so far. Secondly, a technique for image processing, descriptor extraction and subsequent handshape classification using a supervised adaptation of self-organizing maps that is called ProbSom. This technique is compared to others in the state of the art, such as Support Vector Machines (SVM), Random Forests, and Neural Networks. The database that was built contains 800 images with 16 LSA handshapes, and is a first step towards building a comprehensive database of Argentinian signs. The ProbSom-based neural classifier, using the proposed descriptor, achieved an accuracy rate above 90%.

We design and analyze reinforcement learning algorithms for Graphon Mean-Field Games (GMFGs). In contrast to previous works that require the precise values of the graphons, we aim to learn the Nash Equilibrium (NE) of the regularized GMFGs when the graphons are unknown. Our contributions are threefold. First, we propose the Proximal Policy Optimization for GMFG (GMFG-PPO) algorithm and show that it converges at a rate of $O(T^{-1/3})$ after $T$ iterations with an estimation oracle, improving on a previous work by Xie et al. (ICML, 2021). Second, using kernel embedding of distributions, we design efficient algorithms to estimate the transition kernels, reward functions, and graphons from sampled agents. Convergence rates are then derived when the positions of the agents are either known or unknown. Results for the combination of the optimization algorithm GMFG-PPO and the estimation algorithm are then provided. These algorithms are the first specifically designed for learning graphons from sampled agents. Finally, the efficacy of the proposed algorithms are corroborated through simulations. These simulations demonstrate that learning the unknown graphons reduces the exploitability effectively.

The ability to learn and refine behavior after deployment has become ever more important for robots as we design them to operate in unstructured environments like households. In this work, we design a new learning system based on large language model (LLM), OLAF, that allows everyday users to teach a robot using verbal corrections when the robot makes mistakes, e.g., by saying "Stop what you're doing. You should move closer to the cup." A key feature of OLAF is its ability to update the robot's visuomotor neural policy based on the verbal feedback to avoid repeating mistakes in the future. This is in contrast to existing LLM-based robotic systems, which only follow verbal commands or corrections but not learn from them. We demonstrate the efficacy of our design in experiments where a user teaches a robot to perform long-horizon manipulation tasks both in simulation and on physical hardware, achieving on average 20.0% improvement in policy success rate. Videos and more results are at https://ut-austin-rpl.github.io/olaf/

Invariances in neural networks are useful and necessary for many tasks. However, the representation of the invariance of most neural network models has not been characterized. We propose measures to quantify the invariance of neural networks in terms of their internal representation. The measures are efficient and interpretable, and can be applied to any neural network model. They are also more sensitive to invariance than previously defined measures. We validate the measures and their properties in the domain of affine transformations and the CIFAR10 and MNIST datasets, including their stability and interpretability. Using the measures, we perform a first analysis of CNN models and show that their internal invariance is remarkably stable to random weight initializations, but not to changes in dataset or transformation. We believe the measures will enable new avenues of research in invariance representation.

Low-Rank Adaptation (LoRA), a parameter-efficient fine-tuning method that leverages low-rank adaptation of weight matrices, has emerged as a prevalent technique for fine-tuning pre-trained models such as large language models and diffusion models. Despite its huge success in practice, the theoretical underpinnings of LoRA have largely remained unexplored. This paper takes the first step to bridge this gap by theoretically analyzing the expressive power of LoRA. We prove that, for fully connected neural networks, LoRA can adapt any model $f$ to accurately represent any smaller target model $\overline{f}$ if LoRA-rank $\geq(\text{width of }f) \times \frac{\text{depth of }\overline{f}}{\text{depth of }f}$. We also quantify the approximation error when LoRA-rank is lower than the threshold. For Transformer networks, we show any model can be adapted to a target model of the same size with rank-$(\frac{\text{embedding size}}{2})$ LoRA adapters.

Collaborative Vehicle Routing is where delivery companies cooperate by sharing their delivery information and performing delivery requests on behalf of each other. This achieves economies of scale and thus reduces cost, greenhouse gas emissions, and road congestion. But which company should partner with whom, and how much should each company be compensated? Traditional game theoretic solution concepts, such as the Shapley value or nucleolus, are difficult to calculate for the real-world problem of Collaborative Vehicle Routing due to the characteristic function scaling exponentially with the number of agents. This would require solving the Vehicle Routing Problem (an NP-Hard problem) an exponential number of times. We therefore propose to model this problem as a coalitional bargaining game where - crucially - agents are not given access to the characteristic function. Instead, we implicitly reason about the characteristic function, and thus eliminate the need to evaluate the VRP an exponential number of times - we only need to evaluate it once. Our contribution is that our decentralised approach is both scalable and considers the self-interested nature of companies. The agents learn using a modified Independent Proximal Policy Optimisation. Our RL agents outperform a strong heuristic bot. The agents correctly identify the optimal coalitions 79% of the time with an average optimality gap of 4.2% and reduction in run-time of 62%.

Backdoor attack is a common threat to deep neural networks. During testing, samples embedded with a backdoor trigger will be misclassified as an adversarial target by a backdoored model, while samples without the backdoor trigger will be correctly classified. In this paper, we present the first certified backdoor detector (CBD), which is based on a novel, adjustable conformal prediction scheme based on our proposed statistic local dominant probability. For any classifier under inspection, CBD provides 1) a detection inference, 2) the condition under which the attacks are guaranteed to be detectable for the same classification domain, and 3) a probabilistic upper bound for the false positive rate. Our theoretical results show that attacks with triggers that are more resilient to test-time noise and have smaller perturbation magnitudes are more likely to be detected with guarantees. Moreover, we conduct extensive experiments on four benchmark datasets considering various backdoor types, such as BadNet, CB, and Blend. CBD achieves comparable or even higher detection accuracy than state-of-the-art detectors, and it in addition provides detection certification. Notably, for backdoor attacks with random perturbation triggers bounded by $\ell_2\leq0.75$ which achieves more than 90\% attack success rate, CBD achieves 100\% (98\%), 100\% (84\%), 98\% (98\%), and 72\% (40\%) empirical (certified) detection true positive rates on the four benchmark datasets GTSRB, SVHN, CIFAR-10, and TinyImageNet, respectively, with low false positive rates.

We study a novel ensemble approach for feature selection based on hierarchical stacking in cases of non-stationarity and limited number of samples with large number of features. Our approach exploits the co-dependency between features using a hierarchical structure. Initially, a machine learning model is trained using a subset of features, and then the model's output is updated using another algorithm with the remaining features to minimize the target loss. This hierarchical structure allows for flexible depth and feature selection. By exploiting feature co-dependency hierarchically, our proposed approach overcomes the limitations of traditional feature selection methods and feature importance scores. The effectiveness of the approach is demonstrated on synthetic and real-life datasets, indicating improved performance with scalability and stability compared to the traditional methods and state-of-the-art approaches.

In many scenarios, one uses a large training set to train a model with the goal of performing well on a smaller testing set with a different distribution. Learning a weight for each data point of the training set is an appealing solution, as it ideally allows one to automatically learn the importance of each training point for generalization on the testing set. This task is usually formalized as a bilevel optimization problem. Classical bilevel solvers are based on a warm-start strategy where both the parameters of the models and the data weights are learned at the same time. We show that this joint dynamic may lead to sub-optimal solutions, for which the final data weights are very sparse. This finding illustrates the difficulty of data reweighting and offers a clue as to why this method is rarely used in practice.

Future wireless communication networks are in a position to move beyond data-centric, device-oriented connectivity and offer intelligent, immersive experiences based on task-oriented connections, especially in the context of the thriving development of pre-trained foundation models (PFM) and the evolving vision of 6G native artificial intelligence (AI). Therefore, redefining modes of collaboration between devices and servers and constructing native intelligence libraries become critically important in 6G. In this paper, we analyze the challenges of achieving 6G native AI from the perspectives of data, intelligence, and networks. Then, we propose a 6G native AI framework based on foundation models, provide a customization approach for intent-aware PFM, present a construction of a task-oriented AI toolkit, and outline a novel cloud-edge-end collaboration paradigm. As a practical use case, we apply this framework for orchestration, achieving the maximum sum rate within a wireless communication system, and presenting preliminary evaluation results. Finally, we outline research directions for achieving native AI in 6G.

Collaborative vehicle routing occurs when carriers collaborate through sharing their transportation requests and performing transportation requests on behalf of each other. This achieves economies of scale, thus reducing cost, greenhouse gas emissions and road congestion. But which carrier should partner with whom, and how much should each carrier be compensated? Traditional game theoretic solution concepts are expensive to calculate as the characteristic function scales exponentially with the number of agents. This would require solving the vehicle routing problem (NP-hard) an exponential number of times. We therefore propose to model this problem as a coalitional bargaining game solved using deep multi-agent reinforcement learning, where - crucially - agents are not given access to the characteristic function. Instead, we implicitly reason about the characteristic function; thus, when deployed in production, we only need to evaluate the expensive post-collaboration vehicle routing problem once. Our contribution is that we are the first to consider both the route allocation problem and gain sharing problem simultaneously - without access to the expensive characteristic function. Through decentralised machine learning, our agents bargain with each other and agree to outcomes that correlate well with the Shapley value - a fair profit allocation mechanism. Importantly, we are able to achieve a reduction in run-time of 88%.

In this work, we theoretically investigate the generalization properties of neural networks (NN) trained by stochastic gradient descent (SGD) algorithm with large learning rates. Under such a training regime, our finding is that, the oscillation of the NN weights caused by the large learning rate SGD training turns out to be beneficial to the generalization of the NN, which potentially improves over the same NN trained by SGD with small learning rates that converges more smoothly. In view of this finding, we call such a phenomenon "benign oscillation". Our theory towards demystifying such a phenomenon builds upon the feature learning perspective of deep learning. Specifically, we consider a feature-noise data generation model that consists of (i) weak features which have a small $\ell_2$-norm and appear in each data point; (ii) strong features which have a larger $\ell_2$-norm but only appear in a certain fraction of all data points; and (iii) noise. We prove that NNs trained by oscillating SGD with a large learning rate can effectively learn the weak features in the presence of those strong features. In contrast, NNs trained by SGD with a small learning rate can only learn the strong features but makes little progress in learning the weak features. Consequently, when it comes to the new testing data which consist of only weak features, the NN trained by oscillating SGD with a large learning rate could still make correct predictions consistently, while the NN trained by small learning rate SGD fails. Our theory sheds light on how large learning rate training benefits the generalization of NNs. Experimental results demonstrate our finding on "benign oscillation".

Incorporating expert demonstrations has empirically helped to improve the sample efficiency of reinforcement learning (RL). This paper quantifies theoretically to what extent this extra information reduces RL's sample complexity. In particular, we study the demonstration-regularized reinforcement learning that leverages the expert demonstrations by KL-regularization for a policy learned by behavior cloning. Our findings reveal that using $N^{\mathrm{E}}$ expert demonstrations enables the identification of an optimal policy at a sample complexity of order $\widetilde{\mathcal{O}}(\mathrm{Poly}(S,A,H)/(\varepsilon^2 N^{\mathrm{E}}))$ in finite and $\widetilde{\mathcal{O}}(\mathrm{Poly}(d,H)/(\varepsilon^2 N^{\mathrm{E}}))$ in linear Markov decision processes, where $\varepsilon$ is the target precision, $H$ the horizon, $A$ the number of action, $S$ the number of states in the finite case and $d$ the dimension of the feature space in the linear case. As a by-product, we provide tight convergence guarantees for the behaviour cloning procedure under general assumptions on the policy classes. Additionally, we establish that demonstration-regularized methods are provably efficient for reinforcement learning from human feedback (RLHF). In this respect, we provide theoretical evidence showing the benefits of KL-regularization for RLHF in tabular and linear MDPs. Interestingly, we avoid pessimism injection by employing computationally feasible regularization to handle reward estimation uncertainty, thus setting our approach apart from the prior works.

A key element of computer-assisted surgery systems is phase recognition of surgical videos. Existing phase recognition algorithms require frame-wise annotation of a large number of videos, which is time and money consuming. In this work we join concepts of graph segmentation with self-supervised learning to derive a random-walk solution for per-frame phase prediction. Furthermore, we utilize within our method two forms of weak supervision: sparse timestamps or few-shot learning. The proposed algorithm enjoys low complexity and can operate in lowdata regimes. We validate our method by running experiments with the public Cholec80 dataset of laparoscopic cholecystectomy videos, demonstrating promising performance in multiple setups.

Forecasts are typically not produced in a vacuum but in a business context, where forecasts are generated on a regular basis and interact with each other. For decisions, it may be important that forecasts do not change arbitrarily, and are stable in some sense. However, this area has received only limited attention in the forecasting literature. In this paper, we explore two types of forecast stability that we call vertical stability and horizontal stability. The existing works in the literature are only applicable to certain base models and extending these frameworks to be compatible with any base model is not straightforward. Furthermore, these frameworks can only stabilise the forecasts vertically. To fill this gap, we propose a simple linear-interpolation-based approach that is applicable to stabilise the forecasts provided by any base model vertically and horizontally. The approach can produce both accurate and stable forecasts. Using N-BEATS, Pooled Regression and LightGBM as the base models, in our evaluation on four publicly available datasets, the proposed framework is able to achieve significantly higher stability and/or accuracy compared to a set of benchmarks including a state-of-the-art forecast stabilisation method across three error metrics and six stability metrics.

Current tools for machine learning fairness only admit a limited range of fairness definitions and have seen little integration with automatic differentiation libraries, despite the central role these libraries play in modern machine learning pipelines. We introduce a framework of fairness regularization terms (fairrets) which quantify bias as modular objectives that are easily integrated in automatic differentiation pipelines. By employing a general definition of fairness in terms of linear-fractional statistics, a wide class of fairrets can be computed efficiently. Experiments show the behavior of their gradients and their utility in enforcing fairness with minimal loss of predictive power compared to baselines. Our contribution includes a PyTorch implementation of the fairret framework.

This paper introduces two key contributions aimed at improving the speed and quality of images generated through inverse diffusion processes. The first contribution involves reparameterizing the diffusion process in terms of the angle on a quarter-circular arc between the image and noise, specifically setting the conventional $\displaystyle \sqrt{\bar{\alpha}}=\cos(\eta)$. This reparameterization eliminates two singularities and allows for the expression of diffusion evolution as a well-behaved ordinary differential equation (ODE). In turn, this allows higher order ODE solvers such as Runge-Kutta methods to be used effectively. The second contribution is to directly estimate both the image ($\mathbf{x}_0$) and noise ($\mathbf{\epsilon}$) using our network, which enables more stable calculations of the update step in the inverse diffusion steps, as accurate estimation of both the image and noise are crucial at different stages of the process. Together with these changes, our model achieves faster generation, with the ability to converge on high-quality images more quickly, and higher quality of the generated images, as measured by metrics such as Frechet Inception Distance (FID), spatial Frechet Inception Distance (sFID), precision, and recall.

Representation learning assumes that real-world data is generated by a few semantically meaningful generative factors (i.e., sources of variation) and aims to discover them in the latent space. These factors are expected to be causally disentangled, meaning that distinct factors are encoded into separate latent variables, and changes in one factor will not affect the values of the others. Compared to statistical independence, causal disentanglement allows more controllable data generation, improved robustness, and better generalization. However, most existing work assumes unconfoundedness in the discovery process, that there are no common causes to the generative factors and thus obtain only statistical independence. In this paper, we recognize the importance of modeling confounders in discovering causal generative factors. Unfortunately, such factors are not identifiable without proper inductive bias. We fill the gap by introducing a framework entitled Confounded-Disentanglement (C-Disentanglement), the first framework that explicitly introduces the inductive bias of confounder via labels from domain expertise. In addition, we accordingly propose an approach to sufficiently identify the causally disentangled factors under any inductive bias of the confounder. We conduct extensive experiments on both synthetic and real-world datasets. Our method demonstrates competitive results compared to various SOTA baselines in obtaining causally disentangled features and downstream tasks under domain shifts.

Recent curriculum Reinforcement Learning (RL) has shown notable progress in solving complex tasks by proposing sequences of surrogate tasks. However, the previous approaches often face challenges when they generate curriculum goals in a high-dimensional space. Thus, they usually rely on manually specified goal spaces. To alleviate this limitation and improve the scalability of the curriculum, we propose a novel curriculum method that automatically defines the semantic goal space which contains vital information for the curriculum process, and suggests curriculum goals over it. To define the semantic goal space, our method discretizes continuous observations via vector quantized-variational autoencoders (VQ-VAE) and restores the temporal relations between the discretized observations by a graph. Concurrently, ours suggests uncertainty and temporal distance-aware curriculum goals that converges to the final goals over the automatically composed goal space. We demonstrate that the proposed method allows efficient explorations in an uninformed environment with raw goal examples only. Also, ours outperforms the state-of-the-art curriculum RL methods on data efficiency and performance, in various goal-reaching tasks even with ego-centric visual inputs.

To correctly use in-context information, language models (LMs) must bind entities to their attributes. For example, given a context describing a "green square" and a "blue circle", LMs must bind the shapes to their respective colors. We analyze LM representations and identify the binding ID mechanism: a general mechanism for solving the binding problem, which we observe in every sufficiently large model from the Pythia and LLaMA families. Using causal interventions, we show that LMs' internal activations represent binding information by attaching binding ID vectors to corresponding entities and attributes. We further show that binding ID vectors form a continuous subspace, in which distances between binding ID vectors reflect their discernability. Overall, our results uncover interpretable strategies in LMs for representing symbolic knowledge in-context, providing a step towards understanding general in-context reasoning in large-scale LMs.

Electroencephalographic (EEG) signals are fundamental to neuroscience research and clinical applications such as brain-computer interfaces and neurological disorder diagnosis. These signals are typically a combination of neurological activity and noise, originating from various sources, including physiological artifacts like ocular and muscular movements. Under this setting, we tackle the challenge of distinguishing neurological activity from noise-related sources. We develop a novel EEG denoising model that operates in the frequency domain, leveraging prior knowledge about noise spectral features to adaptively compute optimal convolutional filters for noise separation. The model is trained to learn an empirical relationship connecting the spectral characteristics of noise and noisy signal to a non-linear transformation which allows signal denoising. Performance evaluation on the EEGdenoiseNet dataset shows that the proposed model achieves optimal results according to both temporal and spectral metrics. The model is found to remove physiological artifacts from input EEG data, thus achieving effective EEG denoising. Indeed, the model performance either matches or outperforms that achieved by benchmark models, proving to effectively remove both muscle and ocular artifacts without the need to perform any training on the particular type of artifact.

Diffusion probabilistic models (DPMs) have exhibited excellent performance for high-fidelity image generation while suffering from inefficient sampling. Recent works accelerate the sampling procedure by proposing fast ODE solvers that leverage the specific ODE form of DPMs. However, they highly rely on specific parameterization during inference (such as noise/data prediction), which might not be the optimal choice. In this work, we propose a novel formulation towards the optimal parameterization during sampling that minimizes the first-order discretization error of the ODE solution. Based on such formulation, we propose \textit{DPM-Solver-v3}, a new fast ODE solver for DPMs by introducing several coefficients efficiently computed on the pretrained model, which we call \textit{empirical model statistics}. We further incorporate multistep methods and a predictor-corrector framework, and propose some techniques for improving sample quality at small numbers of function evaluations (NFE) or large guidance scales. Experiments show that DPM-Solver-v3 achieves consistently better or comparable performance in both unconditional and conditional sampling with both pixel-space and latent-space DPMs, especially in 5$\sim$10 NFEs. We achieve FIDs of 12.21 (5 NFE), 2.51 (10 NFE) on unconditional CIFAR10, and MSE of 0.55 (5 NFE, 7.5 guidance scale) on Stable Diffusion, bringing a speed-up of 15\%$\sim$30\% compared to previous state-of-the-art training-free methods. Code is available at \url{https://github.com/thu-ml/DPM-Solver-v3}.

There have recently been significant advances in the problem of unsupervised object-centric representation learning and its application to downstream tasks. The latest works support the argument that employing disentangled object representations in image-based object-centric reinforcement learning tasks facilitates policy learning. We propose a novel object-centric reinforcement learning algorithm combining actor-critic and model-based approaches to utilize these representations effectively. In our approach, we use a transformer encoder to extract object representations and graph neural networks to approximate the dynamics of an environment. The proposed method fills a research gap in developing efficient object-centric world models for reinforcement learning settings that can be used for environments with discrete or continuous action spaces. Our algorithm performs better in a visually complex 3D robotic environment and a 2D environment with compositional structure than the state-of-the-art model-free actor-critic algorithm built upon transformer architecture and the state-of-the-art monolithic model-based algorithm.

Offline reinforcement learning (RL) aims to optimize policy using collected data without online interactions. Model-based approaches are particularly appealing for addressing offline RL challenges due to their capability to mitigate the limitations of offline data through data generation using models. Prior research has demonstrated that introducing conservatism into the model or Q-function during policy optimization can effectively alleviate the prevalent distribution drift problem in offline RL. However, the investigation into the impacts of conservatism in reward estimation is still lacking. This paper proposes a novel model-based offline RL algorithm, Conservative Reward for model-based Offline Policy optimization (CROP), which conservatively estimates the reward in model training. To achieve a conservative reward estimation, CROP simultaneously minimizes the estimation error and the reward of random actions. Theoretical analysis shows that this conservative reward mechanism leads to a conservative policy evaluation and helps mitigate distribution drift. Experiments on D4RL benchmarks showcase that the performance of CROP is comparable to the state-of-the-art baselines. Notably, CROP establishes an innovative connection between offline and online RL, highlighting that offline RL problems can be tackled by adopting online RL techniques to the empirical Markov decision process trained with a conservative reward. The source code is available with https://github.com/G0K0URURI/CROP.git.

Along with recent diffusion models, randomized smoothing has become one of a few tangible approaches that offers adversarial robustness to models at scale, e.g., those of large pre-trained models. Specifically, one can perform randomized smoothing on any classifier via a simple "denoise-and-classify" pipeline, so-called denoised smoothing, given that an accurate denoiser is available - such as diffusion model. In this paper, we present scalable methods to address the current trade-off between certified robustness and accuracy in denoised smoothing. Our key idea is to "selectively" apply smoothing among multiple noise scales, coined multi-scale smoothing, which can be efficiently implemented with a single diffusion model. This approach also suggests a new objective to compare the collective robustness of multi-scale smoothed classifiers, and questions which representation of diffusion model would maximize the objective. To address this, we propose to further fine-tune diffusion model (a) to perform consistent denoising whenever the original image is recoverable, but (b) to generate rather diverse outputs otherwise. Our experiments show that the proposed multi-scale smoothing scheme combined with diffusion fine-tuning enables strong certified robustness available with high noise level while maintaining its accuracy closer to non-smoothed classifiers.

Graph Neural Network (GNN) research has produced strategies to modify a graph's edges using gradients from a trained GNN, with the goal of network design. However, the factors which govern gradient-based editing are understudied, obscuring why edges are chosen and if edits are grounded in an edge's importance. Thus, we begin by analyzing the gradient computation in previous works, elucidating the factors that influence edits and highlighting the potential over-reliance on structural properties. Specifically, we find that edges can achieve high gradients due to structural biases, rather than importance, leading to erroneous edits when the factors are unrelated to the design task. To improve editing, we propose ORE, an iterative editing method that (a) edits the highest scoring edges and (b) re-embeds the edited graph to refresh gradients, leading to less biased edge choices. We empirically study ORE through a set of proposed design tasks, each with an external validation method, demonstrating that ORE improves upon previous methods by up to 50%.

In the current study, our purpose is to evaluate the feasibility of applying deep learning (DL) enabled algorithms to quantify bilateral knee biomarkers in healthy controls scanned at 0.55T and compared with 3.0T. The current study assesses the performance of standard in-practice bone, and cartilage segmentation algorithms at 0.55T, both qualitatively and quantitatively, in terms of comparing segmentation performance, areas of improvement, and compartment-wise cartilage thickness values between 0.55T vs. 3.0T. Initial results demonstrate a usable to good technical feasibility of translating existing quantitative deep-learning-based image segmentation techniques, trained on 3.0T, out of 0.55T for knee MRI, in a multi-vendor acquisition environment. Especially in terms of segmenting cartilage compartments, the models perform almost equivalent to 3.0T in terms of Likert ranking. The 0.55T low-field sustainable and easy-to-install MRI, as demonstrated, thus, can be utilized for evaluating knee cartilage thickness and bone segmentations aided by established DL algorithms trained at higher-field strengths out-of-the-box initially. This could be utilized at the far-spread point-of-care locations with a lack of radiologists available to manually segment low-field images, at least till a decent base of low-field data pool is collated. With further fine-tuning with manual labeling of low-field data or utilizing synthesized higher SNR images from low-field images, OA biomarker quantification performance is potentially guaranteed to be further improved.

Deep reinforcement learning (RL) has shown immense potential for learning to control systems through data alone. However, one challenge deep RL faces is that the full state of the system is often not observable. When this is the case, the policy needs to leverage the history of observations to infer the current state. At the same time, differences between the training and testing environments makes it critical for the policy not to overfit to the sequence of observations it sees at training time. As such, there is an important balancing act between having the history encoder be flexible enough to extract relevant information, yet be robust to changes in the environment. To strike this balance, we look to the PID controller for inspiration. We assert the PID controller's success shows that only summing and differencing are needed to accumulate information over time for many control tasks. Following this principle, we propose two architectures for encoding history: one that directly uses PID features and another that extends these core ideas and can be used in arbitrary control tasks. When compared with prior approaches, our encoders produce policies that are often more robust and achieve better performance on a variety of tracking tasks. Going beyond tracking tasks, our policies achieve 1.7x better performance on average over previous state-of-the-art methods on a suite of locomotion control tasks.

Deep learning (DL) models for tabular data problems (e.g. classification, regression) are currently receiving increasingly more attention from researchers. However, despite the recent efforts, the non-DL algorithms based on gradient-boosted decision trees (GBDT) remain a strong go-to solution for these problems. One of the research directions aimed at improving the position of tabular DL involves designing so-called retrieval-augmented models. For a target object, such models retrieve other objects (e.g. the nearest neighbors) from the available training data and use their features and labels to make a better prediction. In this work, we present TabR -- essentially, a feed-forward network with a custom k-Nearest-Neighbors-like component in the middle. On a set of public benchmarks with datasets up to several million objects, TabR marks a big step forward for tabular DL: it demonstrates the best average performance among tabular DL models, becomes the new state-of-the-art on several datasets, and even outperforms GBDT models on the recently proposed "GBDT-friendly" benchmark (see Figure 1). Among the important findings and technical details powering TabR, the main ones lie in the attention-like mechanism that is responsible for retrieving the nearest neighbors and extracting valuable signal from them. In addition to the much higher performance, TabR is simple and significantly more efficient compared to prior retrieval-based tabular DL models.

We present a novel extension to the family of Soft Actor-Critic (SAC) algorithms. We argue that based on the Maximum Entropy Principle, discrete SAC can be further improved via additional statistical constraints derived from a surrogate critic policy. Furthermore, our findings suggests that these constraints provide an added robustness against potential domain shifts, which are essential for safe deployment of reinforcement learning agents in the real-world. We provide theoretical analysis and show empirical results on low data regimes for both in-distribution and out-of-distribution variants of Atari 2600 games.

Recent progress in natural language processing has been adapted to the symbolic music modality. Language models, such as Transformers, have been used with symbolic music for a variety of tasks among which music generation, modeling or transcription, with state-of-the-art performances. These models are beginning to be used in production products. To encode and decode music for the backbone model, they need to rely on tokenizers, whose role is to serialize music into sequences of distinct elements called tokens. MidiTok is an open-source library allowing to tokenize symbolic music with great flexibility and extended features. It features the most popular music tokenizations, under a unified API. It is made to be easily used and extensible for everyone.

Graph neural networks (GNNs) have become increasingly popular for classification tasks on graph-structured data. Yet, the interplay between graph topology and feature evolution in GNNs is not well understood. In this paper, we focus on node-wise classification, illustrated with community detection on stochastic block model graphs, and explore the feature evolution through the lens of the "Neural Collapse" (NC) phenomenon. When training instance-wise deep classifiers (e.g. for image classification) beyond the zero training error point, NC demonstrates a reduction in the deepest features' within-class variability and an increased alignment of their class means to certain symmetric structures. We start with an empirical study that shows that a decrease in within-class variability is also prevalent in the node-wise classification setting, however, not to the extent observed in the instance-wise case. Then, we theoretically study this distinction. Specifically, we show that even an "optimistic" mathematical model requires that the graphs obey a strict structural condition in order to possess a minimizer with exact collapse. Interestingly, this condition is viable also for heterophilic graphs and relates to recent empirical studies on settings with improved GNNs' generalization. Furthermore, by studying the gradient dynamics of the theoretical model, we provide reasoning for the partial collapse observed empirically. Finally, we present a study on the evolution of within- and between-class feature variability across layers of a well-trained GNN and contrast the behavior with spectral methods.

In this paper, we propose a novel deep learning method based on a new Hybrid ConvNet-Transformer architecture to detect and stage Parkinson's disease (PD) from gait data. We adopt a two-step approach by dividing the problem into two sub-problems. Our Hybrid ConvNet-Transformer model first distinguishes healthy versus parkinsonian patients. If the patient is parkinsonian, a multi-class Hybrid ConvNet-Transformer model determines the Hoehn and Yahr (H&Y) score to assess the PD severity stage. Our hybrid architecture exploits the strengths of both Convolutional Neural Networks (ConvNets) and Transformers to accurately detect PD and determine the severity stage. In particular, we take advantage of ConvNets to capture local patterns and correlations in the data, while we exploit Transformers for handling long-term dependencies in the input signal. We show that our hybrid method achieves superior performance when compared to other state-of-the-art methods, with a PD detection accuracy of 97% and a severity staging accuracy of 87%. Our source code is available at: https://github.com/SafwenNaimi

Self-training is a well-known approach for semi-supervised learning. It consists of iteratively assigning pseudo-labels to unlabeled data for which the model is confident and treating them as labeled examples. For neural networks, softmax prediction probabilities are often used as a confidence measure, despite the fact that they are known to be overconfident, even for wrong predictions. This phenomenon is particularly intensified in the presence of sample selection bias, i.e., when data labeling is subject to some constraint. To address this issue, we propose a novel confidence measure, called $\mathcal{T}$-similarity, built upon the prediction diversity of an ensemble of linear classifiers. We provide the theoretical analysis of our approach by studying stationary points and describing the relationship between the diversity of the individual members and their performance. We empirically demonstrate the benefit of our confidence measure for three different pseudo-labeling policies on classification datasets of various data modalities.

Entity and Relation Extraction (ERE) is an important task in information extraction. Recent marker-based pipeline models achieve state-of-the-art performance, but still suffer from the error propagation issue. Also, most of current ERE models do not take into account higher-order interactions between multiple entities and relations, while higher-order modeling could be beneficial.In this work, we propose HyperGraph neural network for ERE ($\hgnn{}$), which is built upon the PL-marker (a state-of-the-art marker-based pipleline model). To alleviate error propagation,we use a high-recall pruner mechanism to transfer the burden of entity identification and labeling from the NER module to the joint module of our model. For higher-order modeling, we build a hypergraph, where nodes are entities (provided by the span pruner) and relations thereof, and hyperedges encode interactions between two different relations or between a relation and its associated subject and object entities. We then run a hypergraph neural network for higher-order inference by applying message passing over the built hypergraph. Experiments on three widely used benchmarks (\acef{}, \ace{} and \scierc{}) for ERE task show significant improvements over the previous state-of-the-art PL-marker.

In recent decades, the use of optical detection systems for meteor studies has increased dramatically, resulting in huge amounts of data being analyzed. Automated meteor detection tools are essential for studying the continuous meteoroid incoming flux, recovering fresh meteorites, and achieving a better understanding of our Solar System. Concerning meteor detection, distinguishing false positives between meteor and non-meteor images has traditionally been performed by hand, which is significantly time-consuming. To address this issue, we developed a fully automated pipeline that uses Convolutional Neural Networks (CNNs) to classify candidate meteor detections. Our new method is able to detect meteors even in images that contain static elements such as clouds, the Moon, and buildings. To accurately locate the meteor within each frame, we employ the Gradient-weighted Class Activation Mapping (Grad-CAM) technique. This method facilitates the identification of the region of interest by multiplying the activations from the last convolutional layer with the average of the gradients across the feature map of that layer. By combining these findings with the activation map derived from the first convolutional layer, we effectively pinpoint the most probable pixel location of the meteor. We trained and evaluated our model on a large dataset collected by the Spanish Meteor Network (SPMN) and achieved a precision of 98\%. Our new methodology presented here has the potential to reduce the workload of meteor scientists and station operators and improve the accuracy of meteor tracking and classification.

In this paper, we present \textbf{C}ont\textbf{E}xtual \textbf{I}mitation \textbf{L}earning~(CEIL), a general and broadly applicable algorithm for imitation learning (IL). Inspired by the formulation of hindsight information matching, we derive CEIL by explicitly learning a hindsight embedding function together with a contextual policy using the hindsight embeddings. To achieve the expert matching objective for IL, we advocate for optimizing a contextual variable such that it biases the contextual policy towards mimicking expert behaviors. Beyond the typical learning from demonstrations (LfD) setting, CEIL is a generalist that can be effectively applied to multiple settings including: 1)~learning from observations (LfO), 2)~offline IL, 3)~cross-domain IL (mismatched experts), and 4) one-shot IL settings. Empirically, we evaluate CEIL on the popular MuJoCo tasks (online) and the D4RL dataset (offline). Compared to prior state-of-the-art baselines, we show that CEIL is more sample-efficient in most online IL tasks and achieves better or competitive performances in offline tasks.

Hallucinations pose a significant challenge to the reliability of neural models for abstractive summarisation. While automatically generated summaries may be fluent, they often lack faithfulness to the original document. This issue becomes even more pronounced in low-resource settings, such as cross-lingual transfer. With the existing faithful metrics focusing on English, even measuring the extent of this phenomenon in cross-lingual settings is hard. To address this, we first develop a novel metric, mFACT, evaluating the faithfulness of non-English summaries, leveraging translation-based transfer from multiple English faithfulness metrics. We then propose a simple but effective method to reduce hallucinations with a cross-lingual transfer, which weighs the loss of each training example by its faithfulness score. Through extensive experiments in multiple languages, we demonstrate that mFACT is the metric that is most suited to detect hallucinations. Moreover, we find that our proposed loss weighting method drastically increases both performance and faithfulness according to both automatic and human evaluation when compared to strong baselines for cross-lingual transfer such as MAD-X. Our code and dataset are available at https://github.com/yfqiu-nlp/mfact-summ.

This paper reexamines the research on out-of-distribution (OOD) robustness in the field of NLP. We find that the distribution shift settings in previous studies commonly lack adequate challenges, hindering the accurate evaluation of OOD robustness. To address these issues, we propose a benchmark construction protocol that ensures clear differentiation and challenging distribution shifts. Then we introduce BOSS, a Benchmark suite for Out-of-distribution robustneSS evaluation covering 5 tasks and 20 datasets. Based on BOSS, we conduct a series of experiments on pre-trained language models for analysis and evaluation of OOD robustness. First, for vanilla fine-tuning, we examine the relationship between in-distribution (ID) and OOD performance. We identify three typical types that unveil the inner learning mechanism, which could potentially facilitate the forecasting of OOD robustness, correlating with the advancements on ID datasets. Then, we evaluate 5 classic methods on BOSS and find that, despite exhibiting some effectiveness in specific cases, they do not offer significant improvement compared to vanilla fine-tuning. Further, we evaluate 5 LLMs with various adaptation paradigms and find that when sufficient ID data is available, fine-tuning domain-specific models outperform LLMs on ID examples significantly. However, in the case of OOD instances, prioritizing LLMs with in-context learning yields better results. We identify that both fine-tuned small models and LLMs face challenges in effectively addressing downstream tasks. The code is public at \url{https://github.com/lifan-yuan/OOD_NLP}.

Cohen et al. (2021) empirically study the evolution of the largest eigenvalue of the loss Hessian, also known as sharpness, along the gradient descent (GD) trajectory and observe the Edge of Stability (EoS) phenomenon. The sharpness increases at the early phase of training (referred to as progressive sharpening), and eventually saturates close to the threshold of $2 / \text{(step size)}$. In this paper, we start by demonstrating through empirical studies that when the EoS phenomenon occurs, different GD trajectories (after a proper reparameterization) align on a specific bifurcation diagram independent of initialization. We then rigorously prove this trajectory alignment phenomenon for a two-layer fully-connected linear network and a single-neuron nonlinear network trained with a single data point. Our trajectory alignment analysis establishes both progressive sharpening and EoS phenomena, encompassing and extending recent findings in the literature.

In this paper, we consider the uncertainty quantification problem for regression models. Specifically, we consider an individual calibration objective for characterizing the quantiles of the prediction model. While such an objective is well-motivated from downstream tasks such as newsvendor cost, the existing methods have been largely heuristic and lack of statistical guarantee in terms of individual calibration. We show via simple examples that the existing methods focusing on population-level calibration guarantees such as average calibration or sharpness can lead to harmful and unexpected results. We propose simple nonparametric calibration methods that are agnostic of the underlying prediction model and enjoy both computational efficiency and statistical consistency. Our approach enables a better understanding of the possibility of individual calibration, and we establish matching upper and lower bounds for the calibration error of our proposed methods. Technically, our analysis combines the nonparametric analysis with a covering number argument for parametric analysis, which advances the existing theoretical analyses in the literature of nonparametric density estimation and quantile bandit problems. Importantly, the nonparametric perspective sheds new theoretical insights into regression calibration in terms of the curse of dimensionality and reconciles the existing results on the impossibility of individual calibration. To our knowledge, we make the first effort to reach both individual calibration and finite-sample guarantee with minimal assumptions in terms of conformal prediction. Numerical experiments show the advantage of such a simple approach under various metrics, and also under covariates shift. We hope our work provides a simple benchmark and a starting point of theoretical ground for future research on regression calibration.

This work bridges two important concepts: the Neural Tangent Kernel (NTK), which captures the evolution of deep neural networks (DNNs) during training, and the Neural Collapse (NC) phenomenon, which refers to the emergence of symmetry and structure in the last-layer features of well-trained classification DNNs. We adopt the natural assumption that the empirical NTK develops a block structure aligned with the class labels, i.e., samples within the same class have stronger correlations than samples from different classes. Under this assumption, we derive the dynamics of DNNs trained with mean squared (MSE) loss and break them into interpretable phases. Moreover, we identify an invariant that captures the essence of the dynamics, and use it to prove the emergence of NC in DNNs with block-structured NTK. We provide large-scale numerical experiments on three common DNN architectures and three benchmark datasets to support our theory.

We introduce a transformation framework that can be utilized to develop online algorithms with low $\epsilon$-approximate regret in the random-order model from offline approximation algorithms. We first give a general reduction theorem that transforms an offline approximation algorithm with low average sensitivity to an online algorithm with low $\epsilon$-approximate regret. We then demonstrate that offline approximation algorithms can be transformed into a low-sensitivity version using a coreset construction method. To showcase the versatility of our approach, we apply it to various problems, including online $(k,z)$-clustering, online matrix approximation, and online regression, and successfully achieve polylogarithmic $\epsilon$-approximate regret for each problem. Moreover, we show that in all three cases, our algorithm also enjoys low inconsistency, which may be desired in some online applications.

Machine learning (ML) has emerged as a prominent field of research in computer science and other related fields, thereby driving advancements in other domains of interest. As the field continues to evolve, it is crucial to understand the landscape of highly cited publications to identify key trends, influential authors, and significant contributions made thus far. In this paper, we present a comprehensive bibliometric analysis of highly cited ML publications. We collected a dataset consisting of the top-cited papers from reputable ML conferences and journals, covering a period of several years from 1959 to 2022. We employed various bibliometric techniques to analyze the data, including citation analysis, co-authorship analysis, keyword analysis, and publication trends. Our findings reveal the most influential papers, highly cited authors, and collaborative networks within the machine learning community. We identify popular research themes and uncover emerging topics that have recently gained significant attention. Furthermore, we examine the geographical distribution of highly cited publications, highlighting the dominance of certain countries in ML research. By shedding light on the landscape of highly cited ML publications, our study provides valuable insights for researchers, policymakers, and practitioners seeking to understand the key developments and trends in this rapidly evolving field.

Graph Neural Network (GNN) research has highlighted a relationship between high homophily (i.e., the tendency of nodes of the same class to connect) and strong predictive performance in node classification. However, recent work has found the relationship to be more nuanced, demonstrating that simple GNNs can learn in certain heterophilous settings. To resolve these conflicting findings and align closer to real-world datasets, we go beyond the assumption of a global graph homophily level and study the performance of GNNs when the local homophily level of a node deviates from the global homophily level. Through theoretical and empirical analysis, we systematically demonstrate how shifts in local homophily can introduce performance degradation, leading to performance discrepancies across local homophily levels. We ground the practical implications of this work through granular analysis on five real-world datasets with varying global homophily levels, demonstrating that (a) GNNs can fail to generalize to test nodes that deviate from the global homophily of a graph, and (b) high local homophily does not necessarily confer high performance for a node. We further show that GNNs designed for globally heterophilous graphs can alleviate performance discrepancy by improving performance across local homophily levels, offering a new perspective on how these GNNs achieve stronger global performance.

Diffusion models (DMs) have recently gained attention with state-of-the-art performance in text-to-image synthesis. Abiding by the tradition in deep learning, DMs are trained and evaluated on the images with fixed sizes. However, users are demanding for various images with specific sizes and various aspect ratio. This paper focuses on adapting text-to-image diffusion models to handle such variety while maintaining visual fidelity. First we observe that, during the synthesis, lower resolution images suffer from incomplete object portrayal, while higher resolution images exhibit repetitively disordered presentation. Next, we establish a statistical relationship indicating that attention entropy changes with token quantity, suggesting that models aggregate spatial information in proportion to image resolution. The subsequent interpretation on our observations is that objects are incompletely depicted due to limited spatial information for low resolutions, while repetitively disorganized presentation arises from redundant spatial information for high resolutions. From this perspective, we propose a scaling factor to alleviate the change of attention entropy and mitigate the defective pattern observed. Extensive experimental results validate the efficacy of the proposed scaling factor, enabling models to achieve better visual effects, image quality, and text alignment. Notably, these improvements are achieved without additional training or fine-tuning techniques.

Massive web datasets play a key role in the success of large vision-language models like CLIP and Flamingo. However, the raw web data is noisy, and existing filtering methods to reduce noise often come at the expense of data diversity. Our work focuses on caption quality as one major source of noise, and studies how generated captions can increase the utility of web-scraped datapoints with nondescript text. Through exploring different mixing strategies for raw and generated captions, we outperform the best filtering method proposed by the DataComp benchmark by 2% on ImageNet and 4% on average across 38 tasks, given a candidate pool of 128M image-text pairs. Our best approach is also 2x better at Flickr and MS-COCO retrieval. We then analyze what makes synthetic captions an effective source of text supervision. In experimenting with different image captioning models, we also demonstrate that the performance of a model on standard image captioning benchmarks (e.g., NoCaps CIDEr) is not a reliable indicator of the utility of the captions it generates for multimodal training. Finally, our experiments with using generated captions at DataComp's large scale (1.28B image-text pairs) offer insights into the limitations of synthetic text, as well as the importance of image curation with increasing training data quantity. The synthetic captions used in our experiments are now available on HuggingFace.

We consider contextual bandit problems with knapsacks [CBwK], a problem where at each round, a scalar reward is obtained and vector-valued costs are suffered. The learner aims to maximize the cumulative rewards while ensuring that the cumulative costs are lower than some predetermined cost constraints. We assume that contexts come from a continuous set, that costs can be signed, and that the expected reward and cost functions, while unknown, may be uniformly estimated -- a typical assumption in the literature. In this setting, total cost constraints had so far to be at least of order $T^{3/4}$, where $T$ is the number of rounds, and were even typically assumed to depend linearly on $T$. We are however motivated to use CBwK to impose a fairness constraint of equalized average costs between groups: the budget associated with the corresponding cost constraints should be as close as possible to the natural deviations, of order $\sqrt{T}$. To that end, we introduce a dual strategy based on projected-gradient-descent updates, that is able to deal with total-cost constraints of the order of $\sqrt{T}$ up to poly-logarithmic terms. This strategy is more direct and simpler than existing strategies in the literature. It relies on a careful, adaptive, tuning of the step size.

Reinforcement learning (RL) has proven to be highly effective in tackling complex decision-making and control tasks. However, prevalent model-free RL methods often face severe performance degradation due to the well-known overestimation issue. In response to this problem, we recently introduced an off-policy RL algorithm, called distributional soft actor-critic (DSAC or DSAC-v1), which can effectively improve the value estimation accuracy by learning a continuous Gaussian value distribution. Nonetheless, standard DSAC has its own shortcomings, including occasionally unstable learning processes and needs for task-specific reward scaling, which may hinder its overall performance and adaptability in some special tasks. This paper further introduces three important refinements to standard DSAC in order to address these shortcomings. These refinements consist of critic gradient adjusting, twin value distribution learning, and variance-based target return clipping. The modified RL algorithm is named as DSAC with three refinements (DSAC-T or DSAC-v2), and its performances are systematically evaluated on a diverse set of benchmark tasks. Without any task-specific hyperparameter tuning, DSAC-T surpasses a lot of mainstream model-free RL algorithms, including SAC, TD3, DDPG, TRPO, and PPO, in all tested environments. Additionally, DSAC-T, unlike its standard version, ensures a highly stable learning process and delivers similar performance across varying reward scales.

Concept bottleneck models have been successfully used for explainable machine learning by encoding information within the model with a set of human-defined concepts. In the context of human-assisted or autonomous driving, explainability models can help user acceptance and understanding of decisions made by the autonomous vehicle, which can be used to rationalize and explain driver or vehicle behavior. We propose a new approach using concept bottlenecks as visual features for control command predictions and explanations of user and vehicle behavior. We learn a human-understandable concept layer that we use to explain sequential driving scenes while learning vehicle control commands. This approach can then be used to determine whether a change in a preferred gap or steering commands from a human (or autonomous vehicle) is led by an external stimulus or change in preferences. We achieve competitive performance to latent visual features while gaining interpretability within our model setup.

The success of over-parameterized neural networks trained to near-zero training error has caused great interest in the phenomenon of benign overfitting, where estimators are statistically consistent even though they interpolate noisy training data. While benign overfitting in fixed dimension has been established for some learning methods, current literature suggests that for regression with typical kernel methods and wide neural networks, benign overfitting requires a high-dimensional setting where the dimension grows with the sample size. In this paper, we show that the smoothness of the estimators, and not the dimension, is the key: benign overfitting is possible if and only if the estimator's derivatives are large enough. We generalize existing inconsistency results to non-interpolating models and more kernels to show that benign overfitting with moderate derivatives is impossible in fixed dimension. Conversely, we show that rate-optimal benign overfitting is possible for regression with a sequence of spiky-smooth kernels with large derivatives. Using neural tangent kernels, we translate our results to wide neural networks. We prove that while infinite-width networks do not overfit benignly with the ReLU activation, this can be fixed by adding small high-frequency fluctuations to the activation function. Our experiments verify that such neural networks, while overfitting, can indeed generalize well even on low-dimensional data sets.

A potent class of generative models known as Diffusion Probabilistic Models (DPMs) has become prominent. A forward diffusion process adds gradually noise to data, while a model learns to gradually denoise. Sampling from pre-trained DPMs is obtained by solving differential equations (DE) defined by the learnt model, a process which has shown to be prohibitively slow. Numerous efforts on speeding-up this process have consisted on crafting powerful ODE solvers. Despite being quick, such solvers do not usually reach the optimal quality achieved by available slow SDE solvers. Our goal is to propose SDE solvers that reach optimal quality without requiring several hundreds or thousands of NFEs to achieve that goal. We propose Stochastic Explicit Exponential Derivative-free Solvers (SEEDS), improving and generalizing Exponential Integrator approaches to the stochastic case on several frameworks. After carefully analyzing the formulation of exact solutions of diffusion SDEs, we craft SEEDS to analytically compute the linear part of such solutions. Inspired by the Exponential Time-Differencing method, SEEDS use a novel treatment of the stochastic components of solutions, enabling the analytical computation of their variance, and contains high-order terms allowing to reach optimal quality sampling $\sim3$-$5\times$ faster than previous SDE methods. We validate our approach on several image generation benchmarks, showing that SEEDS outperform or are competitive with previous SDE solvers. Contrary to the latter, SEEDS are derivative and training free, and we fully prove strong convergence guarantees for them.

We propose a novel and practical privacy notion called $f$-Membership Inference Privacy ($f$-MIP), which explicitly considers the capabilities of realistic adversaries under the membership inference attack threat model. Consequently, $f$-MIP offers interpretable privacy guarantees and improved utility (e.g., better classification accuracy). In particular, we derive a parametric family of $f$-MIP guarantees that we refer to as $\mu$-Gaussian Membership Inference Privacy ($\mu$-GMIP) by theoretically analyzing likelihood ratio-based membership inference attacks on stochastic gradient descent (SGD). Our analysis highlights that models trained with standard SGD already offer an elementary level of MIP. Additionally, we show how $f$-MIP can be amplified by adding noise to gradient updates. Our analysis further yields an analytical membership inference attack that offers two distinct advantages over previous approaches. First, unlike existing state-of-the-art attacks that require training hundreds of shadow models, our attack does not require any shadow model. Second, our analytical attack enables straightforward auditing of our privacy notion $f$-MIP. Finally, we quantify how various hyperparameters (e.g., batch size, number of model parameters) and specific data characteristics determine an attacker's ability to accurately infer a point's membership in the training set. We demonstrate the effectiveness of our method on models trained on vision and tabular datasets.

Separating signals from an additive mixture may be an unnecessarily hard problem when one is only interested in specific properties of a given signal. In this work, we tackle simpler "statistical component separation" problems that focus on recovering a predefined set of statistical descriptors of a target signal from a noisy mixture. Assuming access to samples of the noise process, we investigate a method devised to match the statistics of the solution candidate corrupted by noise samples with those of the observed mixture. We first analyze the behavior of this method using simple examples with analytically tractable calculations. Then, we apply it in an image denoising context employing 1) wavelet-based descriptors, 2) ConvNet-based descriptors on astrophysics and ImageNet data. In the case of 1), we show that our method better recovers the descriptors of the target data than a standard denoising method in most situations. Additionally, despite not constructed for this purpose, it performs surprisingly well in terms of peak signal-to-noise ratio on full signal reconstruction. In comparison, representation 2) appears less suitable for image denoising. Finally, we extend this method by introducing a diffusive stepwise algorithm which gives a new perspective to the initial method and leads to promising results for image denoising under specific circumstances.

We introduce an adaptive sampling method for the Deep Ritz method aimed at solving partial differential equations (PDEs). Two deep neural networks are used. One network is employed to approximate the solution of PDEs, while the other one is a deep generative model used to generate new collocation points to refine the training set. The adaptive sampling procedure consists of two main steps. The first step is solving the PDEs using the Deep Ritz method by minimizing an associated variational loss discretized by the collocation points in the training set. The second step involves generating a new training set, which is then used in subsequent computations to further improve the accuracy of the current approximate solution. We treat the integrand in the variational loss as an unnormalized probability density function (PDF) and approximate it using a deep generative model called bounded KRnet. The new samples and their associated PDF values are obtained from the bounded KRnet. With these new samples and their associated PDF values, the variational loss can be approximated more accurately by importance sampling. Compared to the original Deep Ritz method, the proposed adaptive method improves accuracy, especially for problems characterized by low regularity and high dimensionality. We demonstrate the effectiveness of our new method through a series of numerical experiments.

Partially Observable Markov Decision Processes (POMDPs) are used to model environments where the full state cannot be perceived by an agent. As such the agent needs to reason taking into account the past observations and actions. However, simply remembering the full history is generally intractable due to the exponential growth in the history space. Maintaining a probability distribution that models the belief over what the true state is can be used as a sufficient statistic of the history, but its computation requires access to the model of the environment and is often intractable. While SOTA algorithms use Recurrent Neural Networks to compress the observation-action history aiming to learn a sufficient statistic, they lack guarantees of success and can lead to sub-optimal policies. To overcome this, we propose the Wasserstein Belief Updater, an RL algorithm that learns a latent model of the POMDP and an approximation of the belief update. Our approach comes with theoretical guarantees on the quality of our approximation ensuring that our outputted beliefs allow for learning the optimal value function.

Randomized smoothing is a leading approach for constructing classifiers that are certifiably robust against adversarial examples. Existing work on randomized smoothing has focused on classifiers with continuous inputs, such as images, where $\ell_p$-norm bounded adversaries are commonly studied. However, there has been limited work for classifiers with discrete or variable-size inputs, such as for source code, which require different threat models and smoothing mechanisms. In this work, we adapt randomized smoothing for discrete sequence classifiers to provide certified robustness against edit distance-bounded adversaries. Our proposed smoothing mechanism randomized deletion (RS-Del) applies random deletion edits, which are (perhaps surprisingly) sufficient to confer robustness against adversarial deletion, insertion and substitution edits. Our proof of certification deviates from the established Neyman-Pearson approach, which is intractable in our setting, and is instead organized around longest common subsequences. We present a case study on malware detection--a binary classification problem on byte sequences where classifier evasion is a well-established threat model. When applied to the popular MalConv malware detection model, our smoothing mechanism RS-Del achieves a certified accuracy of 91% at an edit distance radius of 128 bytes.

Recurrent neural networks (RNNs), originally developed for natural language processing, hold great promise for accurately describing strongly correlated quantum many-body systems. Here, we employ 2D RNNs to investigate two prototypical quantum many-body Hamiltonians exhibiting topological order. Specifically, we demonstrate that RNN wave functions can effectively capture the topological order of the toric code and a Bose-Hubbard spin liquid on the kagome lattice by estimating their topological entanglement entropies. We also find that RNNs favor coherent superpositions of minimally-entangled states over minimally-entangled states themselves. Overall, our findings demonstrate that RNN wave functions constitute a powerful tool to study phases of matter beyond Landau's symmetry-breaking paradigm.

The question of what makes a data distribution suitable for deep learning is a fundamental open problem. Focusing on locally connected neural networks (a prevalent family of architectures that includes convolutional and recurrent neural networks as well as local self-attention models), we address this problem by adopting theoretical tools from quantum physics. Our main theoretical result states that a certain locally connected neural network is capable of accurate prediction over a data distribution if and only if the data distribution admits low quantum entanglement under certain canonical partitions of features. As a practical application of this result, we derive a preprocessing method for enhancing the suitability of a data distribution to locally connected neural networks. Experiments with widespread models over various datasets demonstrate our findings. We hope that our use of quantum entanglement will encourage further adoption of tools from physics for formally reasoning about the relation between deep learning and real-world data.

Breaking down a problem into intermediate steps has demonstrated impressive performance in Large Language Model (LLM) reasoning. However, the growth of the reasoning chain introduces uncertainty and error accumulation, making it challenging to elicit accurate final results. To tackle this challenge of uncertainty in multi-step reasoning, we introduce a stepwise self-evaluation mechanism to guide and calibrate the reasoning process of LLMs. We propose a decoding algorithm integrating the self-evaluation guidance via stochastic beam search. The self-evaluation guidance serves as a better-calibrated automatic criterion, facilitating an efficient search in the reasoning space and resulting in superior prediction quality. Stochastic beam search balances exploitation and exploration of the search space with temperature-controlled randomness. Our approach surpasses the corresponding Codex-backboned baselines in few-shot accuracy by $6.34\%$, $9.56\%$, and $5.46\%$ on the GSM8K, AQuA, and StrategyQA benchmarks, respectively. Experiment results with Llama-2 on arithmetic reasoning demonstrate the efficiency of our method in outperforming the baseline methods with comparable computational budgets. Further analysis in multi-step reasoning finds our self-evaluation guidance pinpoints logic failures and leads to higher consistency and robustness. Our code is publicly available at https://guideddecoding.github.io/.

Artificial intelligence (AI) is entering medical imaging, mainly enhancing image reconstruction. Nevertheless, improvements throughout the entire processing, from signal detection to computation, potentially offer significant benefits. This work presents a novel and versatile approach to detector optimization using machine learning (ML) and residual physics. We apply the concept to positron emission tomography (PET), intending to improve the coincidence time resolution (CTR). PET visualizes metabolic processes in the body by detecting photons with scintillation detectors. Improved CTR performance offers the advantage of reducing radioactive dose exposure for patients. Modern PET detectors with sophisticated concepts and read-out topologies represent complex physical and electronic systems requiring dedicated calibration techniques. Traditional methods primarily depend on analytical formulations successfully describing the main detector characteristics. However, when accounting for higher-order effects, additional complexities arise matching theoretical models to experimental reality. Our work addresses this challenge by combining traditional calibration with AI and residual physics, presenting a highly promising approach. We present a residual physics-based strategy using gradient tree boosting and physics-guided data generation. The explainable AI framework SHapley Additive exPlanations (SHAP) was used to identify known physical effects with learned patterns. In addition, the models were tested against basic physical laws. We were able to improve the CTR significantly (more than 20%) for clinically relevant detectors of 19 mm height, reaching CTRs of 185 ps (450-550 keV).

Transformer-based speech self-supervised learning (SSL) models, such as HuBERT, show surprising performance in various speech processing tasks. However, huge number of parameters in speech SSL models necessitate the compression to a more compact model for wider usage in academia or small companies. In this study, we suggest to reuse attention maps across the Transformer layers, so as to remove key and query parameters while retaining the number of layers. Furthermore, we propose a novel masking distillation strategy to improve the student model's speech representation quality. We extend the distillation loss to utilize both masked and unmasked speech frames to fully leverage the teacher model's high-quality representation. Our universal compression strategy yields the student model that achieves phoneme error rate (PER) of 7.72% and word error rate (WER) of 9.96% on the SUPERB benchmark.

Gaussian process (GP) hyperparameter optimization requires repeatedly solving linear systems with $n \times n$ kernel matrices. To address the prohibitive $\mathcal{O}(n^3)$ time complexity, recent work has employed fast iterative numerical methods, like conjugate gradients (CG). However, as datasets increase in magnitude, the corresponding kernel matrices become increasingly ill-conditioned and still require $\mathcal{O}(n^2)$ space without partitioning. Thus, while CG increases the size of datasets GPs can be trained on, modern datasets reach scales beyond its applicability. In this work, we propose an iterative method which only accesses subblocks of the kernel matrix, effectively enabling \emph{mini-batching}. Our algorithm, based on alternating projection, has $\mathcal{O}(n)$ per-iteration time and space complexity, solving many of the practical challenges of scaling GPs to very large datasets. Theoretically, we prove our method enjoys linear convergence and empirically we demonstrate its robustness to ill-conditioning. On large-scale benchmark datasets up to four million datapoints our approach accelerates training by a factor of 2$\times$ to 27$\times$ compared to CG.

While bisimulation-based approaches hold promise for learning robust state representations for Reinforcement Learning (RL) tasks, their efficacy in offline RL tasks has not been up to par. In some instances, their performance has even significantly underperformed alternative methods. We aim to understand why bisimulation methods succeed in online settings, but falter in offline tasks. Our analysis reveals that missing transitions in the dataset are particularly harmful to the bisimulation principle, leading to ineffective estimation. We also shed light on the critical role of reward scaling in bounding the scale of bisimulation measurements and of the value error they induce. Based on these findings, we propose to apply the expectile operator for representation learning to our offline RL setting, which helps to prevent overfitting to incomplete data. Meanwhile, by introducing an appropriate reward scaling strategy, we avoid the risk of feature collapse in representation space. We implement these recommendations on two state-of-the-art bisimulation-based algorithms, MICo and SimSR, and demonstrate performance gains on two benchmark suites: D4RL and Visual D4RL. Codes are provided at \url{https://github.com/zanghyu/Offline_Bisimulation}.

Maximum likelihood estimation (MLE) is a statistical method used to estimate the parameters of a probability distribution that best explain the observed data. In the context of text generation, MLE is often used to train generative language models, which can then be used to generate new text. However, we argue that MLE is not always necessary and optimal, especially for closed-ended text generation tasks like machine translation. In these tasks, the goal of model is to generate the most appropriate response, which does not necessarily require it to estimate the entire data distribution with MLE. To this end, we propose a novel class of training objectives based on convex functions, which enables text generation models to focus on highly probable outputs without having to estimate the entire data distribution. We investigate the theoretical properties of the optimal predicted distribution when applying convex functions to the loss, demonstrating that convex functions can sharpen the optimal distribution, thereby enabling the model to better capture outputs with high probabilities. Experiments on various text generation tasks and models show the effectiveness of our approach. It enables autoregressive models to bridge the gap between greedy and beam search, and facilitates the learning of non-autoregressive models with a maximum improvement of 9+ BLEU points. Moreover, our approach also exhibits significant impact on large language models (LLMs), substantially enhancing their generative capability on various tasks. Source code is available at \url{https://github.com/ictnlp/Convex-Learning}.

A significant level of stigma and inequality exists in mental healthcare, especially in under-served populations. Inequalities are reflected in the data collected for scientific purposes. When not properly accounted for, machine learning (ML) models leart from data can reinforce these structural inequalities or biases. Here, we present a systematic study of bias in ML models designed to predict depression in four different case studies covering different countries and populations. We find that standard ML approaches show regularly biased behaviors. We also show that mitigation techniques, both standard and our own post-hoc method, can be effective in reducing the level of unfair bias. No single best ML model for depression prediction provides equality of outcomes. This emphasizes the importance of analyzing fairness during model selection and transparent reporting about the impact of debiasing interventions. Finally, we provide practical recommendations to develop bias-aware ML models for depression risk prediction.

We introduce a suite of new particle-based algorithms for sampling on constrained domains which are entirely learning rate free. Our approach leverages coin betting ideas from convex optimisation, and the viewpoint of constrained sampling as a mirrored optimisation problem on the space of probability measures. Based on this viewpoint, we also introduce a unifying framework for several existing constrained sampling algorithms, including mirrored Langevin dynamics and mirrored Stein variational gradient descent. We demonstrate the performance of our algorithms on a range of numerical examples, including sampling from targets on the simplex, sampling with fairness constraints, and constrained sampling problems in post-selection inference. Our results indicate that our algorithms achieve competitive performance with existing constrained sampling methods, without the need to tune any hyperparameters.

Simulation-based inference has been popular for amortized Bayesian computation. It is typical to have more than one posterior approximation, from different inference algorithms, different architectures, or simply the randomness of initialization and stochastic gradients. With a provable asymptotic guarantee, we present a general stacking framework to make use of all available posterior approximations. Our stacking method is able to combine densities, simulation draws, confidence intervals, and moments, and address the overall precision, calibration, coverage, and bias at the same time. We illustrate our method on several benchmark simulations and a challenging cosmological inference task.

Lichens are symbiotic organisms composed of fungi, algae, and/or cyanobacteria that thrive in a variety of environments. They play important roles in carbon and nitrogen cycling, and contribute directly and indirectly to biodiversity. Ecologists typically monitor lichens by using them as indicators to assess air quality and habitat conditions. In particular, epiphytic lichens, which live on trees, are key markers of air quality and environmental health. A new method of monitoring epiphytic lichens involves using time-lapse cameras to gather images of lichen populations. These cameras are used by ecologists in Newfoundland and Labrador to subsequently analyze and manually segment the images to determine lichen thalli condition and change. These methods are time-consuming and susceptible to observer bias. In this work, we aim to automate the monitoring of lichens over extended periods and to estimate their biomass and condition to facilitate the task of ecologists. To accomplish this, our proposed framework uses semantic segmentation with an effective training approach to automate monitoring and biomass estimation of epiphytic lichens on time-lapse images. We show that our method has the potential to significantly improve the accuracy and efficiency of lichen population monitoring, making it a valuable tool for forest ecologists and environmental scientists to evaluate the impact of climate change on Canada's forests. To the best of our knowledge, this is the first time that such an approach has been used to assist ecologists in monitoring and analyzing epiphytic lichens.

The breakthrough performance of large language models (LLMs) comes with major computational footprints and high deployment costs. In this paper, we progress towards resolving this problem by proposing a novel structured compression approach for LLMs, called ZipLM. ZipLM achieves state-of-the-art accuracy-vs-speedup, while matching a set of desired target runtime speedups in any given inference environment. Specifically, given a model, a dataset, an inference environment, as well as a set of speedup targets, ZipLM iteratively identifies and removes components with the worst loss-runtime trade-off. Unlike prior methods that specialize in either the post-training/one-shot or the gradual compression setting, and only for specific families of models such as BERT (encoder) or GPT (decoder), ZipLM produces state-of-the-art compressed models across all these settings. Furthermore, ZipLM achieves superior results for a fraction of the computational cost relative to prior distillation and pruning techniques, making it a cost-effective approach for generating an entire family of smaller, faster, and highly accurate models, guaranteed to meet the desired inference specifications. In particular, ZipLM outperforms all prior BERT-base distillation and pruning techniques, such as CoFi, MiniLM, and TinyBERT. Moreover, it matches the performance of the heavily optimized MobileBERT model, obtained via extensive architecture search, by simply pruning the baseline BERT-large model. When compressing GPT2, ZipLM outperforms DistilGPT2 while being 60% smaller and 30% faster. Our code is available at: https://github.com/IST-DASLab/ZipLM.

Large learning rates, when applied to gradient descent for nonconvex optimization, yield various implicit biases including the edge of stability (Cohen et al., 2021), balancing (Wang et al., 2022), and catapult (Lewkowycz et al., 2020). These phenomena cannot be well explained by classical optimization theory. Though significant theoretical progress has been made in understanding these implicit biases, it remains unclear for which objective functions would they occur. This paper provides an initial step in answering this question, namely that these implicit biases are in fact various tips of the same iceberg. They occur when the objective function of optimization has some good regularity, which, in combination with a provable preference of large learning rate gradient descent for moving toward flatter regions, results in these nontrivial dynamical phenomena. To establish this result, we develop a new global convergence theory under large learning rates, for a family of nonconvex functions without globally Lipschitz continuous gradient, which was typically assumed in existing convergence analysis. A byproduct is the first non-asymptotic convergence rate bound for large-learning-rate gradient descent optimization of nonconvex functions. We also validate our theory with experiments on neural networks, where different losses, activation functions, and batch normalization all can significantly affect regularity and lead to very different training dynamics.

Spatio-temporal graph neural networks (STGNNs) have gained popularity as a powerful tool for effectively modeling spatio-temporal dependencies in diverse real-world urban applications, including intelligent transportation and public safety. However, the black-box nature of STGNNs limits their interpretability, hindering their application in scenarios related to urban resource allocation and policy formulation. To bridge this gap, we propose an Explainable Spatio-Temporal Graph Neural Networks (STExplainer) framework that enhances STGNNs with inherent explainability, enabling them to provide accurate predictions and faithful explanations simultaneously. Our framework integrates a unified spatio-temporal graph attention network with a positional information fusion layer as the STG encoder and decoder, respectively. Furthermore, we propose a structure distillation approach based on the Graph Information Bottleneck (GIB) principle with an explainable objective, which is instantiated by the STG encoder and decoder. Through extensive experiments, we demonstrate that our STExplainer outperforms state-of-the-art baselines in terms of predictive accuracy and explainability metrics (i.e., sparsity and fidelity) on traffic and crime prediction tasks. Furthermore, our model exhibits superior representation ability in alleviating data missing and sparsity issues. The implementation code is available at: https://github.com/HKUDS/STExplainer.

As artificial intelligence (AI) gains greater adoption in a wide variety of applications, it has immense potential to contribute to mathematical discovery, by guiding conjecture generation, constructing counterexamples, assisting in formalizing mathematics, and discovering connections between different mathematical areas, to name a few. While prior work has leveraged computers for exhaustive mathematical proof search, recent efforts based on large language models (LLMs) aspire to position computing platforms as co-contributors in the mathematical research process. Despite their current limitations in logic and mathematical tasks, there is growing interest in melding theorem proving systems with foundation models. This work investigates the applicability of LLMs in formalizing advanced mathematical concepts and proposes a framework that can critically review and check mathematical reasoning in research papers. Given the noted reasoning shortcomings of LLMs, our approach synergizes the capabilities of proof assistants, specifically PVS, with LLMs, enabling a bridge between textual descriptions in academic papers and formal specifications in PVS. By harnessing the PVS environment, coupled with data ingestion and conversion mechanisms, we envision an automated process, called \emph{math-PVS}, to extract and formalize mathematical theorems from research papers, offering an innovative tool for academic review and discovery.

In an era marked by the increasing adoption of Large Language Models (LLMs) for various tasks, there is a growing focus on exploring LLMs' capabilities in handling web data, particularly graph data. Dynamic graphs, which capture temporal network evolution patterns, are ubiquitous in real-world web data. Evaluating LLMs' competence in understanding spatial-temporal information on dynamic graphs is essential for their adoption in web applications, which remains unexplored in the literature. In this paper, we bridge the gap via proposing to evaluate LLMs' spatial-temporal understanding abilities on dynamic graphs, to the best of our knowledge, for the first time. Specifically, we propose the LLM4DyG benchmark, which includes nine specially designed tasks considering the capability evaluation of LLMs from both temporal and spatial dimensions. Then, we conduct extensive experiments to analyze the impacts of different data generators, data statistics, prompting techniques, and LLMs on the model performance. Finally, we propose Disentangled Spatial-Temporal Thoughts (DST2) for LLMs on dynamic graphs to enhance LLMs' spatial-temporal understanding abilities. Our main observations are: 1) LLMs have preliminary spatial-temporal understanding abilities on dynamic graphs, 2) Dynamic graph tasks show increasing difficulties for LLMs as the graph size and density increase, while not sensitive to the time span and data generation mechanism, 3) the proposed DST2 prompting method can help to improve LLMs' spatial-temporal understanding abilities on dynamic graphs for most tasks. The data and codes will be open-sourced at publication time.

Semi-implicit variational inference (SIVI) has been introduced to expand the analytical variational families by defining expressive semi-implicit distributions in a hierarchical manner. However, the single-layer architecture commonly used in current SIVI methods can be insufficient when the target posterior has complicated structures. In this paper, we propose hierarchical semi-implicit variational inference, called HSIVI, which generalizes SIVI to allow more expressive multi-layer construction of semi-implicit distributions. By introducing auxiliary distributions that interpolate between a simple base distribution and the target distribution, the conditional layers can be trained by progressively matching these auxiliary distributions one layer after another. Moreover, given pre-trained score networks, HSIVI can be used to accelerate the sampling process of diffusion models with the score matching objective. We show that HSIVI significantly enhances the expressiveness of SIVI on several Bayesian inference problems with complicated target distributions. When used for diffusion model acceleration, we show that HSIVI can produce high quality samples comparable to or better than the existing fast diffusion model based samplers with a small number of function evaluations on various datasets.

Large language models (LLMs) with hundreds of billions of parameters have sparked a new wave of exciting AI applications. However, they are computationally expensive at inference time. Sparsity is a natural approach to reduce this cost, but existing methods either require costly retraining, have to forgo LLM's in-context learning ability, or do not yield wall-clock time speedup on modern hardware. We hypothesize that contextual sparsity, which are small, input-dependent sets of attention heads and MLP parameters that yield approximately the same output as the dense model for a given input, can address these issues. We show that contextual sparsity exists, that it can be accurately predicted, and that we can exploit it to speed up LLM inference in wall-clock time without compromising LLM's quality or in-context learning ability. Based on these insights, we propose DejaVu, a system that uses a low-cost algorithm to predict contextual sparsity on the fly given inputs to each layer, along with an asynchronous and hardware-aware implementation that speeds up LLM inference. We validate that DejaVu can reduce the inference latency of OPT-175B by over 2X compared to the state-of-the-art FasterTransformer, and over 6X compared to the widely used Hugging Face implementation, without compromising model quality. The code is available at https://github.com/FMInference/DejaVu.

In some settings neural networks exhibit a phenomenon known as grokking, where they achieve perfect or near-perfect accuracy on the validation set long after the same performance has been achieved on the training set. In this paper, we discover that grokking is not limited to neural networks but occurs in other settings such as Gaussian process (GP) classification, GP regression and linear regression. We also uncover a mechanism by which to induce grokking on algorithmic datasets via the addition of dimensions containing spurious information. The presence of the phenomenon in non-neural architectures provides evidence that grokking is not specific to SGD or weight norm regularisation. Instead, grokking may be possible in any setting where solution search is guided by complexity and error. Based on this insight and further trends we see in the training trajectories of a Bayesian neural network (BNN) and GP regression model, we make progress towards a more general theory of grokking. Specifically, we hypothesise that the phenomenon is governed by the accessibility of certain regions in the error and complexity landscapes.

We consider the application of machine learning to the evaluation of geothermal resource potential. A supervised learning problem is defined where maps of 10 geological and geophysical features within the state of Nevada, USA are used to define geothermal potential across a broad region. We have available a relatively small set of positive training sites (known resources or active power plants) and negative training sites (known drill sites with unsuitable geothermal conditions) and use these to constrain and optimize artificial neural networks for this classification task. The main objective is to predict the geothermal resource potential at unknown sites within a large geographic area where the defining features are known. These predictions could be used to target promising areas for further detailed investigations. We describe the evolution of our work from defining a specific neural network architecture to training and optimization trials. Upon analysis we expose the inevitable problems of model variability and resulting prediction uncertainty. Finally, to address these problems we apply the concept of Bayesian neural networks, a heuristic approach to regularization in network training, and make use of the practical interpretation of the formal uncertainty measures they provide.

Breaking down a document or a conversation into multiple contiguous segments based on its semantic structure is an important and challenging problem in NLP, which can assist many downstream tasks. However, current works on topic segmentation often focus on segmentation of structured texts. In this paper, we comprehensively analyze the generalization capabilities of state-of-the-art topic segmentation models on unstructured texts. We find that: (a) Current strategies of pre-training on a large corpus of structured text such as Wiki-727K do not help in transferability to unstructured conversational data. (b) Training from scratch with only a relatively small-sized dataset of the target unstructured domain improves the segmentation results by a significant margin. We stress-test our proposed Topic Segmentation approach by experimenting with multiple loss functions, in order to mitigate effects of imbalance in unstructured conversational datasets. Our empirical evaluation indicates that Focal Loss function is a robust alternative to Cross-Entropy and re-weighted Cross-Entropy loss function when segmenting unstructured and semi-structured chats.

Understanding neural networks is challenging in part because of the dense, continuous nature of their hidden states. We explore whether we can train neural networks to have hidden states that are sparse, discrete, and more interpretable by quantizing their continuous features into what we call codebook features. Codebook features are produced by finetuning neural networks with vector quantization bottlenecks at each layer, producing a network whose hidden features are the sum of a small number of discrete vector codes chosen from a larger codebook. Surprisingly, we find that neural networks can operate under this extreme bottleneck with only modest degradation in performance. This sparse, discrete bottleneck also provides an intuitive way of controlling neural network behavior: first, find codes that activate when the desired behavior is present, then activate those same codes during generation to elicit that behavior. We validate our approach by training codebook Transformers on several different datasets. First, we explore a finite state machine dataset with far more hidden states than neurons. In this setting, our approach overcomes the superposition problem by assigning states to distinct codes, and we find that we can make the neural network behave as if it is in a different state by activating the code for that state. Second, we train Transformer language models with up to 410M parameters on two natural language datasets. We identify codes in these models representing diverse, disentangled concepts (ranging from negative emotions to months of the year) and find that we can guide the model to generate different topics by activating the appropriate codes during inference. Overall, codebook features appear to be a promising unit of analysis and control for neural networks and interpretability. Our codebase and models are open-sourced at https://github.com/taufeeque9/codebook-features.

We present a new loss function that addresses the out-of-distribution (OOD) calibration problem. While many objective functions have been proposed to effectively calibrate models in-distribution, our findings show that they do not always fare well OOD. Based on the Principle of Maximum Entropy, we incorporate helpful statistical constraints observed during training, delivering better model calibration without sacrificing accuracy. We provide theoretical analysis and show empirically that our method works well in practice, achieving state-of-the-art calibration on both synthetic and real-world benchmarks.

Neural additive models (NAMs) can improve the interpretability of deep neural networks by handling input features in separate additive sub-networks. However, they lack inherent mechanisms that provide calibrated uncertainties and enable selection of relevant features and interactions. Approaching NAMs from a Bayesian perspective, we enhance them in three primary ways, namely by a) providing credible intervals for the individual additive sub-networks; b) estimating the marginal likelihood to perform an implicit selection of features via an empirical Bayes procedure; and c) enabling a ranking of feature pairs as candidates for second-order interaction in fine-tuned models. In particular, we develop Laplace-approximated NAMs (LA-NAMs), which show improved empirical performance on tabular datasets and challenging real-world medical tasks.

Recent contrastive language image pre-training has led to learning highly transferable and robust image representations. However, adapting these models to video domains with minimal supervision remains an open problem. We explore a simple step in that direction, using language tied self-supervised learning to adapt an image CLIP model to the video domain. A backbone modified for temporal modeling is trained under self-distillation settings with train objectives operating in an action concept space. Feature vectors of various action concepts extracted from a language encoder using relevant textual prompts construct this space. We introduce two train objectives, concept distillation and concept alignment, that retain generality of original representations while enforcing relations between actions and their attributes. Our approach improves zero-shot and linear probing performance on three action recognition benchmarks.

Robustness has been extensively studied in reinforcement learning (RL) to handle various forms of uncertainty such as random perturbations, rare events, and malicious attacks. In this work, we consider one critical type of robustness against spurious correlation, where different portions of the state do not have correlations induced by unobserved confounders. These spurious correlations are ubiquitous in real-world tasks, for instance, a self-driving car usually observes heavy traffic in the daytime and light traffic at night due to unobservable human activity. A model that learns such useless or even harmful correlation could catastrophically fail when the confounder in the test case deviates from the training one. Although motivated, enabling robustness against spurious correlation poses significant challenges since the uncertainty set, shaped by the unobserved confounder and causal structure, is difficult to characterize and identify. Existing robust algorithms that assume simple and unstructured uncertainty sets are therefore inadequate to address this challenge. To solve this issue, we propose Robust State-Confounded Markov Decision Processes (RSC-MDPs) and theoretically demonstrate its superiority in avoiding learning spurious correlations compared with other robust RL counterparts. We also design an empirical algorithm to learn the robust optimal policy for RSC-MDPs, which outperforms all baselines in eight realistic self-driving and manipulation tasks.

Large Language Models (LLMs) and Large Multimodal Models (LMMs) exhibit impressive problem-solving skills in many tasks and domains, but their ability in mathematical reasoning in visual contexts has not been systematically studied. To bridge this gap, we present MathVista, a benchmark designed to combine challenges from diverse mathematical and visual tasks. It consists of 6,141 examples, derived from 28 existing multimodal datasets involving mathematics and 3 newly created datasets (i.e., IQTest, FunctionQA, and PaperQA). Completing these tasks requires fine-grained, deep visual understanding and compositional reasoning, which all state-of-the-art foundation models find challenging. With MathVista, we have conducted a comprehensive, quantitative evaluation of 12 prominent foundation models. The best-performing GPT-4V model achieves an overall accuracy of 49.9%, substantially outperforming Bard, the second-best performer, by 15.1%. Our in-depth analysis reveals that the superiority of GPT-4V is mainly attributed to its enhanced visual perception and mathematical reasoning. However, GPT-4V still falls short of human performance by 10.4%, as it often struggles to understand complex figures and perform rigorous reasoning. This significant gap underscores the critical role that MathVista will play in the development of general-purpose AI agents capable of tackling mathematically intensive and visually rich real-world tasks. We further explore the new ability of self-verification, the application of self-consistency, and the interactive chatbot capabilities of GPT-4V, highlighting its promising potential for future research. The project is available at https://mathvista.github.io/.

Offline reinforcement learning (RL) offers an appealing approach to real-world tasks by learning policies from pre-collected datasets without interacting with the environment. However, the performance of existing offline RL algorithms heavily depends on the scale and state-action space coverage of datasets. Real-world data collection is often expensive and uncontrollable, leading to small and narrowly covered datasets and posing significant challenges for practical deployments of offline RL. In this paper, we provide a new insight that leveraging the fundamental symmetry of system dynamics can substantially enhance offline RL performance under small datasets. Specifically, we propose a Time-reversal symmetry (T-symmetry) enforced Dynamics Model (TDM), which establishes consistency between a pair of forward and reverse latent dynamics. TDM provides both well-behaved representations for small datasets and a new reliability measure for OOD samples based on compliance with the T-symmetry. These can be readily used to construct a new offline RL algorithm (TSRL) with less conservative policy constraints and a reliable latent space data augmentation procedure. Based on extensive experiments, we find TSRL achieves great performance on small benchmark datasets with as few as 1% of the original samples, which significantly outperforms the recent offline RL algorithms in terms of data efficiency and generalizability.Code is available at: https://github.com/pcheng2/TSRL

The evaluation of clustering algorithms can involve running them on a variety of benchmark problems, and comparing their outputs to the reference, ground-truth groupings provided by experts. Unfortunately, many research papers and graduate theses consider only a small number of datasets. Also, the fact that there can be many equally valid ways to cluster a given problem set is rarely taken into account. In order to overcome these limitations, we have developed a framework whose aim is to introduce a consistent methodology for testing clustering algorithms. Furthermore, we have aggregated, polished, and standardised many clustering benchmark dataset collections referred to across the machine learning and data mining literature, and included new datasets of different dimensionalities, sizes, and cluster types. An interactive datasets explorer, the documentation of the Python API, a description of the ways to interact with the framework from other programming languages such as R or MATLAB, and other details are all provided at .

We present COMEDIAN, a novel pipeline to initialize spatiotemporal transformers for action spotting, which involves self-supervised learning and knowledge distillation. Action spotting is a timestamp-level temporal action detection task. Our pipeline consists of three steps, with two initialization stages. First, we perform self-supervised initialization of a spatial transformer using short videos as input. Additionally, we initialize a temporal transformer that enhances the spatial transformer's outputs with global context through knowledge distillation from a pre-computed feature bank aligned with each short video segment. In the final step, we fine-tune the transformers to the action spotting task. The experiments, conducted on the SoccerNet-v2 dataset, demonstrate state-of-the-art performance and validate the effectiveness of COMEDIAN's pretraining paradigm. Our results highlight several advantages of our pretraining pipeline, including improved performance and faster convergence compared to non-pretrained models.

Pretrained machine learning models are known to perpetuate and even amplify existing biases in data, which can result in unfair outcomes that ultimately impact user experience. Therefore, it is crucial to understand the mechanisms behind those prejudicial biases to ensure that model performance does not result in discriminatory behaviour toward certain groups or populations. In this work, we define gender bias as our case study. We quantify bias amplification in pretraining and after fine-tuning on three families of vision-and-language models. We investigate the connection, if any, between the two learning stages, and evaluate how bias amplification reflects on model performance. Overall, we find that bias amplification in pretraining and after fine-tuning are independent. We then examine the effect of continued pretraining on gender-neutral data, finding that this reduces group disparities, i.e., promotes fairness, on VQAv2 and retrieval tasks without significantly compromising task performance.

Recurrent neural networks (RNNs) are popular machine learning tools for modeling and forecasting sequential data and for inferring dynamical systems (DS) from observed time series. Concepts from DS theory (DST) have variously been used to further our understanding of both, how trained RNNs solve complex tasks, and the training process itself. Bifurcations are particularly important phenomena in DS, including RNNs, that refer to topological (qualitative) changes in a system's dynamical behavior as one or more of its parameters are varied. Knowing the bifurcation structure of an RNN will thus allow to deduce many of its computational and dynamical properties, like its sensitivity to parameter variations or its behavior during training. In particular, bifurcations may account for sudden loss jumps observed in RNN training that could severely impede the training process. Here we first mathematically prove for a particular class of ReLU-based RNNs that certain bifurcations are indeed associated with loss gradients tending toward infinity or zero. We then introduce a novel heuristic algorithm for detecting all fixed points and k-cycles in ReLU-based RNNs and their existence and stability regions, hence bifurcation manifolds in parameter space. In contrast to previous numerical algorithms for finding fixed points and common continuation methods, our algorithm provides exact results and returns fixed points and cycles up to high orders with surprisingly good scaling behavior. We exemplify the algorithm on the analysis of the training process of RNNs, and find that the recently introduced technique of generalized teacher forcing completely avoids certain types of bifurcations in training. Thus, besides facilitating the DST analysis of trained RNNs, our algorithm provides a powerful instrument for analyzing the training process itself.

When perceiving the world from multiple viewpoints, humans have the ability to reason about the complete objects in a compositional manner even when an object is completely occluded from certain viewpoints. Meanwhile, humans are able to imagine novel views after observing multiple viewpoints. Recent remarkable advances in multi-view object-centric learning still leaves some unresolved problems: 1) The shapes of partially or completely occluded objects can not be well reconstructed. 2) The novel viewpoint prediction depends on expensive viewpoint annotations rather than implicit rules in view representations. In this paper, we introduce a time-conditioned generative model for videos. To reconstruct the complete shape of an object accurately, we enhance the disentanglement between the latent representations of objects and views, where the latent representations of time-conditioned views are jointly inferred with a Transformer and then are input to a sequential extension of Slot Attention to learn object-centric representations. In addition, Gaussian processes are employed as priors of view latent variables for video generation and novel-view prediction without viewpoint annotations. Experiments on multiple datasets demonstrate that the proposed model can make object-centric video decomposition, reconstruct the complete shapes of occluded objects, and make novel-view predictions.

In this study, we propose a novel multi-objective Bayesian optimization (MOBO) method to efficiently identify the Pareto front (PF) defined by risk measures for black-box functions under the presence of input uncertainty (IU). Existing BO methods for Pareto optimization in the presence of IU are risk-specific or without theoretical guarantees, whereas our proposed method addresses general risk measures and has theoretical guarantees. The basic idea of the proposed method is to assume a Gaussian process (GP) model for the black-box function and to construct high-probability bounding boxes for the risk measures using the GP model. Furthermore, in order to reduce the uncertainty of non-dominated bounding boxes, we propose a method of selecting the next evaluation point using a maximin distance defined by the maximum value of a quasi distance based on bounding boxes. As theoretical analysis, we prove that the algorithm can return an arbitrary-accurate solution in a finite number of iterations with high probability, for various risk measures such as Bayes risk, worst-case risk, and value-at-risk. We also give a theoretical analysis that takes into account approximation errors because there exist non-negligible approximation errors (e.g., finite approximation of PFs and sampling-based approximation of bounding boxes) in practice. We confirm that the proposed method outperforms compared with existing methods not only in the setting with IU but also in the setting of ordinary MOBO through numerical experiments.

We describe a method for verifying the output of a deep neural network for medical image segmentation that is robust to several classes of random as well as worst-case perturbations i.e. adversarial attacks. This method is based on a general approach recently developed by the authors called ``Trust, but Verify" wherein an auxiliary verification network produces predictions about certain masked features in the input image using the segmentation as an input. A well-designed auxiliary network will produce high-quality predictions when the input segmentations are accurate, but will produce low-quality predictions when the segmentations are incorrect. Checking the predictions of such a network with the original image allows us to detect bad segmentations. However, to ensure the verification method is truly robust, we need a method for checking the quality of the predictions that does not itself rely on a black-box neural network. Indeed, we show that previous methods for segmentation evaluation that do use deep neural regression networks are vulnerable to false negatives i.e. can inaccurately label bad segmentations as good. We describe the design of a verification network that avoids such vulnerability and present results to demonstrate its robustness compared to previous methods.

Generative diffusion models have achieved spectacular performance in many areas of generative modeling. While the fundamental ideas behind these models come from non-equilibrium physics, in this paper we show that many aspects of these models can be understood using the tools of equilibrium statistical mechanics. Using this reformulation, we show that generative diffusion models undergo second-order phase transitions corresponding to symmetry breaking phenomena. We argue that this lead to a form of instability that lies at the heart of their generative capabilities and that can be described by a set of mean field critical exponents. We conclude by analyzing recent work connecting diffusion models and associative memory networks in view of the thermodynamic formulations.

Biases with respect to socially-salient attributes of individuals have been well documented in evaluation processes used in settings such as admissions and hiring. We view such an evaluation process as a transformation of a distribution of the true utility of an individual for a task to an observed distribution and model it as a solution to a loss minimization problem subject to an information constraint. Our model has two parameters that have been identified as factors leading to biases: the resource-information trade-off parameter in the information constraint and the risk-averseness parameter in the loss function. We characterize the distributions that arise from our model and study the effect of the parameters on the observed distribution. The outputs of our model enrich the class of distributions that can be used to capture variation across groups in the observed evaluations. We empirically validate our model by fitting real-world datasets and use it to study the effect of interventions in a downstream selection task. These results contribute to an understanding of the emergence of bias in evaluation processes and provide tools to guide the deployment of interventions to mitigate biases.

Transformer-based large language models have remarkable potential to accelerate design optimization for applications such as drug development and materials discovery. Self-supervised pretraining of transformer models requires large-scale datasets, which are often sparsely populated in topical areas such as polymer science. State-of-the-art approaches for polymers conduct data augmentation to generate additional samples but unavoidably incurs extra computational costs. In contrast, large-scale open-source datasets are available for small molecules and provide a potential solution to data scarcity through transfer learning. In this work, we show that using transformers pretrained on small molecules and fine-tuned on polymer properties achieve comparable accuracy to those trained on augmented polymer datasets for a series of benchmark prediction tasks.

Large language models (LLMs) such as GPT-3 have demonstrated a strong capability to generate coherent and contextually relevant text. However, amidst their successes, a crucial issue persists: their generated outputs still lack commonsense at times. Moreover, fine-tuning the entire LLM towards more commonsensical outputs is computationally expensive if not infeasible. In this paper, we present a computation-efficient framework that steers a frozen Pre-Trained Language Model (PTLM) towards more commonsensical generation (i.e., producing a plausible output that incorporates a list of concepts in a meaningful way). Specifically, we first construct a reference-free evaluator that assigns a sentence with a commonsensical score by grounding the sentence to a dynamic commonsense knowledge base from four different relational aspects. We then use the scorer as the oracle for commonsense knowledge, and extend the controllable generation method called NADO to train an auxiliary head that guides a fixed PTLM to better satisfy the oracle. We test our framework on a series of GPT-2-, Flan-T5-, and Alpaca-based language models (LMs) on two constrained concept-to-sentence benchmarks. Human evaluation results demonstrate that our method consistently leads to the most commonsensical outputs.

The decision tree is a flexible machine learning model that finds its success in numerous applications. It is usually fitted in a recursively greedy manner using CART. In this paper, we investigate the convergence rate of CART under a regression setting. First, we establish an upper bound on the prediction error of CART under a sufficient impurity decrease (SID) condition \cite{chi2022asymptotic} -- our result improves upon the known result by \cite{chi2022asymptotic} under a similar assumption. Furthermore, we provide examples that demonstrate the error bound cannot be further improved by more than a constant or a logarithmic factor. Second, we introduce a set of easily verifiable sufficient conditions for the SID condition. Specifically, we demonstrate that the SID condition can be satisfied in the case of an additive model, provided that the component functions adhere to a ``locally reverse Poincar{\'e} inequality". We discuss several well-known function classes in non-parametric estimation to illustrate the practical utility of this concept.

We propose a simple generalization of standard and empirically successful decision tree learning algorithms such as ID3, C4.5, and CART. These algorithms, which have been central to machine learning for decades, are greedy in nature: they grow a decision tree by iteratively splitting on the best attribute. Our algorithm, Top-$k$, considers the $k$ best attributes as possible splits instead of just the single best attribute. We demonstrate, theoretically and empirically, the power of this simple generalization. We first prove a {\sl greediness hierarchy theorem} showing that for every $k \in \mathbb{N}$, Top-$(k+1)$ can be dramatically more powerful than Top-$k$: there are data distributions for which the former achieves accuracy $1-\varepsilon$, whereas the latter only achieves accuracy $\frac1{2}+\varepsilon$. We then show, through extensive experiments, that Top-$k$ outperforms the two main approaches to decision tree learning: classic greedy algorithms and more recent "optimal decision tree" algorithms. On one hand, Top-$k$ consistently enjoys significant accuracy gains over greedy algorithms across a wide range of benchmarks. On the other hand, Top-$k$ is markedly more scalable than optimal decision tree algorithms and is able to handle dataset and feature set sizes that remain far beyond the reach of these algorithms.

Positioned between pre-training and user deployment, aligning large language models (LLMs) through reinforcement learning (RL) has emerged as a prevailing strategy for training instruction following-models such as ChatGPT. In this work, we initiate the study of privacy-preserving alignment of LLMs through Differential Privacy (DP) in conjunction with RL. Following the influential work of Ziegler et al. (2020), we study two dominant paradigms: (i) alignment via RL without human in the loop (e.g., positive review generation) and (ii) alignment via RL from human feedback (RLHF) (e.g., summarization in a human-preferred way). We give a new DP framework to achieve alignment via RL, and prove its correctness. Our experimental results validate the effectiveness of our approach, offering competitive utility while ensuring strong privacy protections.

Active learning is a promising paradigm to reduce the labeling cost by strategically requesting labels to improve model performance. However, existing active learning methods often rely on expensive acquisition function to compute, extensive modeling retraining and multiple rounds of interaction with annotators. To address these limitations, we propose a novel approach for active learning, which aims to select batches of unlabeled instances through a learned surrogate model for data acquisition. A key challenge in this approach is developing an acquisition function that generalizes well, as the history of data, which forms part of the utility function's input, grows over time. Our novel algorithmic contribution is a bilevel multi-task bilevel optimization framework that predicts the relative utility -- measured by the validation accuracy -- of different training sets, and ensures the learned acquisition function generalizes effectively. For cases where validation accuracy is expensive to evaluate, we introduce efficient interpolation-based surrogate models to estimate the utility function, reducing the evaluation cost. We demonstrate the performance of our approach through extensive experiments on standard active classification benchmarks. By employing our learned utility function, we show significant improvements over traditional techniques, paving the way for more efficient and effective utility maximization in active learning applications.

Exploring the convergence of electric vehicles (EVs), renewable energy, and smart grid technologies in the context of Texas, this study addresses challenges hindering the widespread adoption of EVs. Acknowledging their environmental benefits, the research focuses on grid stability concerns, uncoordinated charging patterns, and the complicated relationship between EVs and renewable energy sources. Dynamic time warping (DTW) clustering and k-means clustering methodologies categorize days based on total load and net load, offering nuanced insights into daily electricity consumption and renewable energy generation patterns. By establishing optimal charging and vehicle-to-grid (V2G) windows tailored to specific load characteristics, the study provides a sophisticated methodology for strategic decision-making in energy consumption and renewable integration. The findings contribute to the ongoing discourse on achieving a sustainable and resilient energy future through the seamless integration of EVs into smart grids.

The Motion Manifold Primitive (MMP) produces, for a given task, a continuous manifold of trajectories each of which can successfully complete the task. It consists of the decoder function that parametrizes the manifold and the probability density in the latent coordinate space. In this paper, we first show that the MMP performance can significantly degrade due to the geometric distortion in the latent space -- by distortion, we mean that similar motions are not located nearby in the latent space. We then propose {\it Isometric Motion Manifold Primitives (IMMP)} whose latent coordinate space preserves the geometry of the manifold. For this purpose, we formulate and use a Riemannian metric for the motion space (i.e., parametric curve space), which we call a {\it CurveGeom Riemannian metric}. Experiments with planar obstacle-avoiding motions and pushing manipulation tasks show that IMMP significantly outperforms existing MMP methods. Code is available at https://github.com/Gabe-YHLee/IMMP-public.

Transformers are remarkably good at in-context learning (ICL) -- learning from demonstrations without parameter updates -- but how they perform ICL remains a mystery. Recent work suggests that Transformers may learn in-context by internally running Gradient Descent, a first-order optimization method. In this paper, we instead demonstrate that Transformers learn to implement higher-order optimization methods to perform ICL. Focusing on in-context linear regression, we show that Transformers learn to implement an algorithm very similar to Iterative Newton's Method, a higher-order optimization method, rather than Gradient Descent. Empirically, we show that predictions from successive Transformer layers closely match different iterations of Newton's Method linearly, with each middle layer roughly computing 3 iterations. In contrast, exponentially more Gradient Descent steps are needed to match an additional Transformers layer; this suggests that Transformers have an comparable rate of convergence with high-order methods such as Iterative Newton, which are exponentially faster than Gradient Descent. We also show that Transformers can learn in-context on ill-conditioned data, a setting where Gradient Descent struggles but Iterative Newton succeeds. Finally, we show theoretical results which support our empirical findings and have a close correspondence with them: we prove that Transformers can implement $k$ iterations of Newton's method with $\mathcal{O}(k)$ layers.

We present two neural network approaches that approximate the solutions of static and dynamic conditional optimal transport (COT) problems, respectively. Both approaches enable sampling and density estimation of conditional probability distributions, which are core tasks in Bayesian inference. Our methods represent the target conditional distributions as transformations of a tractable reference distribution and, therefore, fall into the framework of measure transport. COT maps are a canonical choice within this framework, with desirable properties such as uniqueness and monotonicity. However, the associated COT problems are computationally challenging, even in moderate dimensions. To improve the scalability, our numerical algorithms leverage neural networks to parameterize COT maps. Our methods exploit the structure of the static and dynamic formulations of the COT problem. PCP-Map models conditional transport maps as the gradient of a partially input convex neural network (PICNN) and uses a novel numerical implementation to increase computational efficiency compared to state-of-the-art alternatives. COT-Flow models conditional transports via the flow of a regularized neural ODE; it is slower to train but offers faster sampling. We demonstrate their effectiveness and efficiency by comparing them with state-of-the-art approaches using benchmark datasets and Bayesian inverse problems.

Synthetic data serves as an alternative in training machine learning models, particularly when real-world data is limited or inaccessible. However, ensuring that synthetic data mirrors the complex nuances of real-world data is a challenging task. This paper addresses this issue by exploring the potential of integrating data-centric AI techniques which profile the data to guide the synthetic data generation process. Moreover, we shed light on the often ignored consequences of neglecting these data profiles during synthetic data generation -- despite seemingly high statistical fidelity. Subsequently, we propose a novel framework to evaluate the integration of data profiles to guide the creation of more representative synthetic data. In an empirical study, we evaluate the performance of five state-of-the-art models for tabular data generation on eleven distinct tabular datasets. The findings offer critical insights into the successes and limitations of current synthetic data generation techniques. Finally, we provide practical recommendations for integrating data-centric insights into the synthetic data generation process, with a specific focus on classification performance, model selection, and feature selection. This study aims to reevaluate conventional approaches to synthetic data generation and promote the application of data-centric AI techniques in improving the quality and effectiveness of synthetic data.

This paper combines two contributions. First, we introduce an extension of the Meta-World benchmark, which we call "Language-World," which allows a large language model to operate in a simulated robotic environment using semi-structured natural language queries and scripted skills described using natural language. By using the same set of tasks as Meta-World, Language-World results can be easily compared to Meta-World results, allowing for a point of comparison between recent methods using Large Language Models (LLMs) and those using Deep Reinforcement Learning. Second, we introduce a method we call Plan Conditioned Behavioral Cloning (PCBC), that allows finetuning the behavior of high-level plans using end-to-end demonstrations. Using Language-World, we show that PCBC is able to achieve strong performance in a variety of few-shot regimes, often achieving task generalization with as little as a single demonstration. We have made Language-World available as open-source software at https://github.com/krzentner/language-world/.

Practical tensor data is often along with time information. Most existing temporal decomposition approaches estimate a set of fixed factors for the objects in each tensor mode, and hence cannot capture the temporal evolution of the objects' representation. More important, we lack an effective approach to capture such evolution from streaming data, which is common in real-world applications. To address these issues, we propose Streaming Factor Trajectory Learning (SFTL) for temporal tensor decomposition. We use Gaussian processes (GPs) to model the trajectory of factors so as to flexibly estimate their temporal evolution. To address the computational challenges in handling streaming data, we convert the GPs into a state-space prior by constructing an equivalent stochastic differential equation (SDE). We develop an efficient online filtering algorithm to estimate a decoupled running posterior of the involved factor states upon receiving new data. The decoupled estimation enables us to conduct standard Rauch-Tung-Striebel smoothing to compute the full posterior of all the trajectories in parallel, without the need for revisiting any previous data. We have shown the advantage of SFTL in both synthetic tasks and real-world applications.

Accurate inference of fine-grained traffic flow from coarse-grained one is an emerging yet crucial problem, which can help greatly reduce the number of the required traffic monitoring sensors for cost savings. In this work, we notice that traffic flow has a high correlation with road network, which was either completely ignored or simply treated as an external factor in previous works. To facilitate this problem, we propose a novel Road-Aware Traffic Flow Magnifier (RATFM) that explicitly exploits the prior knowledge of road networks to fully learn the road-aware spatial distribution of fine-grained traffic flow. Specifically, a multi-directional 1D convolutional layer is first introduced to extract the semantic feature of the road network. Subsequently, we incorporate the road network feature and coarse-grained flow feature to regularize the short-range spatial distribution modeling of road-relative traffic flow. Furthermore, we take the road network feature as a query to capture the long-range spatial distribution of traffic flow with a transformer architecture. Benefiting from the road-aware inference mechanism, our method can generate high-quality fine-grained traffic flow maps. Extensive experiments on three real-world datasets show that the proposed RATFM outperforms state-of-the-art models under various scenarios. Our code and datasets are released at {\url{https://github.com/luimoli/RATFM}}.

Label embedding is a framework for multiclass classification problems where each label is represented by a distinct vector of some fixed dimension, and training involves matching model output to the vector representing the correct label. While label embedding has been successfully applied in extreme classification and zero-shot learning, and offers both computational and statistical advantages, its theoretical foundations remain poorly understood. This work presents an analysis of label embedding in the context of extreme multiclass classification, where the number of classes $C$ is very large. We present an excess risk bound that reveals a trade-off between computational and statistical efficiency, quantified via the coherence of the embedding matrix. We further show that under the Massart noise condition, the statistical penalty for label embedding vanishes with sufficiently low coherence. Our analysis supports an algorithm that is simple, scalable, and easily parallelizable, and experimental results demonstrate its effectiveness in large-scale applications.

We propose controlled decoding (CD), a novel off-policy reinforcement learning method to control the autoregressive generation from language models towards high reward outcomes. CD solves an off-policy reinforcement learning problem through a value function for the reward, which we call a prefix scorer. The prefix scorer is used at inference time to steer the generation towards higher reward outcomes. We show that the prefix scorer may be trained on (possibly) off-policy data to predict the expected reward when decoding is continued from a partially decoded response. We empirically demonstrate that CD is effective as a control mechanism on Reddit conversations corpus. We also show that the modularity of the design of CD makes it possible to control for multiple rewards, effectively solving a multi-objective reinforcement learning problem with no additional complexity. Finally, we show that CD can be applied in a novel blockwise fashion at inference-time, again without the need for any training-time changes, essentially bridging the gap between the popular best-of-$K$ strategy and token-level reinforcement learning. This makes CD a promising approach for alignment of language models.

Learning phone types from phone instances has been a long-standing problem, while still being open. In this work, we revisit this problem in the context of self-supervised learning, and pose it as the problem of matching cluster centroids to phone embeddings. We study two key properties that enable matching, namely, whether cluster centroids of self-supervised representations reduce the variability of phone instances and respect the relationship among phones. We then use the matching result to produce pseudo-labels and introduce a new loss function for improving self-supervised representations. Our experiments show that the matching result captures the relationship among phones. Training the new loss function jointly with the regular self-supervised losses, such as APC and CPC, significantly improves the downstream phone classification.

Lithium metal battery (LMB) has the potential to be the next-generation battery system because of its high theoretical energy density. However, defects known as dendrites are formed by heterogeneous lithium (Li) plating, which hinders the development and utilization of LMBs. Non-destructive techniques to observe the dendrite morphology often use X-ray computed tomography (XCT) to provide cross-sectional views. To retrieve three-dimensional structures inside a battery, image segmentation becomes essential to quantitatively analyze XCT images. This work proposes a new semantic segmentation approach using a transformer-based neural network called TransforCNN that is capable of segmenting out dendrites from XCT data. In addition, we compare the performance of the proposed TransforCNN with three other algorithms, such as U-Net, Y-Net, and E-Net, consisting of an Ensemble Network model for XCT analysis. Our results show the advantages of using TransforCNN when evaluating over-segmentation metrics, such as mean Intersection over Union (mIoU) and mean Dice Similarity Coefficient (mDSC) as well as through several qualitatively comparative visualizations.

Variable importance assessment has become a crucial step in machine-learning applications when using complex learners, such as deep neural networks, on large-scale data. Removal-based importance assessment is currently the reference approach, particularly when statistical guarantees are sought to justify variable inclusion. It is often implemented with variable permutation schemes. On the flip side, these approaches risk misidentifying unimportant variables as important in the presence of correlations among covariates. Here we develop a systematic approach for studying Conditional Permutation Importance (CPI) that is model agnostic and computationally lean, as well as reusable benchmarks of state-of-the-art variable importance estimators. We show theoretically and empirically that $\textit{CPI}$ overcomes the limitations of standard permutation importance by providing accurate type-I error control. When used with a deep neural network, $\textit{CPI}$ consistently showed top accuracy across benchmarks. An experiment on real-world data analysis in a large-scale medical dataset showed that $\textit{CPI}$ provides a more parsimonious selection of statistically significant variables. Our results suggest that $\textit{CPI}$ can be readily used as drop-in replacement for permutation-based methods.

Like many optimizers, Bayesian optimization often falls short of gaining user trust due to opacity. While attempts have been made to develop human-centric optimizers, they typically assume user knowledge is well-specified and error-free, employing users mainly as supervisors of the optimization process. We relax these assumptions and propose a more balanced human-AI partnership with our Collaborative and Explainable Bayesian Optimization (CoExBO) framework. Instead of explicitly requiring a user to provide a knowledge model, CoExBO employs preference learning to seamlessly integrate human insights into the optimization, resulting in algorithmic suggestions that resonate with user preference. CoExBO explains its candidate selection every iteration to foster trust, empowering users with a clearer grasp of the optimization. Furthermore, CoExBO offers a no-harm guarantee, allowing users to make mistakes; even with extreme adversarial interventions, the algorithm converges asymptotically to a vanilla Bayesian optimization. We validate CoExBO's efficacy through human-AI teaming experiments in lithium-ion battery design, highlighting substantial improvements over conventional methods.

Deep reinforcement learning methods exhibit impressive performance on a range of tasks but still struggle on hard exploration tasks in large environments with sparse rewards. To address this, intrinsic rewards can be generated using forward model prediction errors that decrease as the environment becomes known, and incentivize an agent to explore novel states. While prediction-based intrinsic rewards can help agents solve hard exploration tasks, they can suffer from catastrophic forgetting and actually increase at visited states. We first examine the conditions and causes of catastrophic forgetting in grid world environments. We then propose a new method FARCuriosity, inspired by how humans and animals learn. The method depends on fragmentation and recall: an agent fragments an environment based on surprisal, and uses different local curiosity modules (prediction-based intrinsic reward functions) for each fragment so that modules are not trained on the entire environment. At each fragmentation event, the agent stores the current module in long-term memory (LTM) and either initializes a new module or recalls a previously stored module based on its match with the current state. With fragmentation and recall, FARCuriosity achieves less forgetting and better overall performance in games with varied and heterogeneous environments in the Atari benchmark suite of tasks. Thus, this work highlights the problem of catastrophic forgetting in prediction-based curiosity methods and proposes a solution.

We propose a new algorithm for efficiently solving the damped Fisher matrix in large-scale scenarios where the number of parameters significantly exceeds the number of available samples. This problem is fundamental for natural gradient descent and stochastic reconfiguration. Our algorithm is based on Cholesky decomposition and is generally applicable. Benchmark results show that the algorithm is significantly faster than existing methods.

Federated Learning (FL) is an emerging distributed learning paradigm under privacy constraint. Data heterogeneity is one of the main challenges in FL, which results in slow convergence and degraded performance. Most existing approaches only tackle the heterogeneity challenge by restricting the local model update in client, ignoring the performance drop caused by direct global model aggregation. Instead, we propose a data-free knowledge distillation method to fine-tune the global model in the server (FedFTG), which relieves the issue of direct model aggregation. Concretely, FedFTG explores the input space of local models through a generator, and uses it to transfer the knowledge from local models to the global model. Besides, we propose a hard sample mining scheme to achieve effective knowledge distillation throughout the training. In addition, we develop customized label sampling and class-level ensemble to derive maximum utilization of knowledge, which implicitly mitigates the distribution discrepancy across clients. Extensive experiments show that our FedFTG significantly outperforms the state-of-the-art (SOTA) FL algorithms and can serve as a strong plugin for enhancing FedAvg, FedProx, FedDyn, and SCAFFOLD.

High sample complexity has long been a challenge for RL. On the other hand, humans learn to perform tasks not only from interaction or demonstrations, but also by reading unstructured text documents, e.g., instruction manuals. Instruction manuals and wiki pages are among the most abundant data that could inform agents of valuable features and policies or task-specific environmental dynamics and reward structures. Therefore, we hypothesize that the ability to utilize human-written instruction manuals to assist learning policies for specific tasks should lead to a more efficient and better-performing agent. We propose the Read and Reward framework. Read and Reward speeds up RL algorithms on Atari games by reading manuals released by the Atari game developers. Our framework consists of a QA Extraction module that extracts and summarizes relevant information from the manual and a Reasoning module that evaluates object-agent interactions based on information from the manual. An auxiliary reward is then provided to a standard A2C RL agent, when interaction is detected. Experimentally, various RL algorithms obtain significant improvement in performance and training speed when assisted by our design.

Numerous works study black-box attacks on image classifiers. However, these works make different assumptions on the adversary's knowledge and current literature lacks a cohesive organization centered around the threat model. To systematize knowledge in this area, we propose a taxonomy over the threat space spanning the axes of feedback granularity, the access of interactive queries, and the quality and quantity of the auxiliary data available to the attacker. Our new taxonomy provides three key insights. 1) Despite extensive literature, numerous under-explored threat spaces exist, which cannot be trivially solved by adapting techniques from well-explored settings. We demonstrate this by establishing a new state-of-the-art in the less-studied setting of access to top-k confidence scores by adapting techniques from well-explored settings of accessing the complete confidence vector, but show how it still falls short of the more restrictive setting that only obtains the prediction label, highlighting the need for more research. 2) Identification the threat model of different attacks uncovers stronger baselines that challenge prior state-of-the-art claims. We demonstrate this by enhancing an initially weaker baseline (under interactive query access) via surrogate models, effectively overturning claims in the respective paper. 3) Our taxonomy reveals interactions between attacker knowledge that connect well to related areas, such as model inversion and extraction attacks. We discuss how advances in other areas can enable potentially stronger black-box attacks. Finally, we emphasize the need for a more realistic assessment of attack success by factoring in local attack runtime. This approach reveals the potential for certain attacks to achieve notably higher success rates and the need to evaluate attacks in diverse and harder settings, highlighting the need for better selection criteria.

The prevailing principle of "Optimism in the Face of Uncertainty" advocates for the incorporation of an exploration bonus, generally assumed to be proportional to the inverse square root of the visit count ($1/\sqrt{n}$), where $n$ is the number of visits to a particular state-action pair. This approach, however, exclusively focuses on "uncertainty," neglecting the inherent "difficulty" of different options. To address this gap, we introduce a novel modification of standard UCB algorithm in the multi-armed bandit problem, proposing an adjusted bonus term of $1/n^\tau$, where $\tau > 1/2$, that accounts for task difficulty. Our proposed algorithm, denoted as UCB$^\tau$, is substantiated through comprehensive regret and risk analyses, confirming its theoretical robustness. Comparative evaluations with standard UCB and Thompson Sampling algorithms on synthetic datasets demonstrate that UCB$^\tau$ not only outperforms in efficacy but also exhibits lower risk across various environmental conditions and hyperparameter settings.

Customizing voice and speaking style in a speech synthesis system with intuitive and fine-grained controls is challenging, given that little data with appropriate labels is available. Furthermore, editing an existing human's voice also comes with ethical concerns. In this paper, we propose a method to generate artificial speaker embeddings that cannot be linked to a real human while offering intuitive and fine-grained control over the voice and speaking style of the embeddings, without requiring any labels for speaker or style. The artificial and controllable embeddings can be fed to a speech synthesis system, conditioned on embeddings of real humans during training, without sacrificing privacy during inference.

The current state-of-the-art decentralized learning algorithms mostly assume the data distribution to be Independent and Identically Distributed (IID). However, in practical scenarios, the distributed datasets can have significantly heterogeneous data distributions across the agents. In this work, we present a novel approach for decentralized learning on heterogeneous data, where data-free knowledge distillation through contrastive loss on cross-features is utilized to improve performance. Cross-features for a pair of neighboring agents are the features (i.e., last hidden layer activations) obtained from the data of an agent with respect to the model parameters of the other agent. We demonstrate the effectiveness of the proposed technique through an exhaustive set of experiments on various Computer Vision datasets (CIFAR-10, CIFAR-100, Fashion MNIST, Imagenette, and ImageNet), model architectures, and network topologies. Our experiments show that the proposed method achieves superior performance (0.2-4% improvement in test accuracy) compared to other existing techniques for decentralized learning on heterogeneous data.

In this paper, we consider the framework of multi-task representation (MTR) learning where the goal is to use source tasks to learn a representation that reduces the sample complexity of solving a target task. We start by reviewing recent advances in MTR theory and show that they can provide novel insights for popular meta-learning algorithms when analyzed within this framework. In particular, we highlight a fundamental difference between gradient-based and metric-based algorithms in practice and put forward a theoretical analysis to explain it. Finally, we use the derived insights to improve the performance of meta-learning methods via a new spectral-based regularization term and confirm its efficiency through experimental studies on few-shot classification benchmarks. To the best of our knowledge, this is the first contribution that puts the most recent learning bounds of MTR theory into practice for the task of few-shot classification.

In the rapidly advancing domain of deep learning optimization, this paper unveils the StochGradAdam optimizer, a novel adaptation of the well-regarded Adam algorithm. Central to StochGradAdam is its gradient sampling technique. This method not only ensures stable convergence but also leverages the advantages of selective gradient consideration, fostering robust training by potentially mitigating the effects of noisy or outlier data and enhancing the exploration of the loss landscape for more dependable convergence. In both image classification and segmentation tasks, StochGradAdam has demonstrated superior performance compared to the traditional Adam optimizer. By judiciously sampling a subset of gradients at each iteration, the optimizer is optimized for managing intricate models. The paper provides a comprehensive exploration of StochGradAdam's methodology, from its mathematical foundations to bias correction strategies, heralding a promising advancement in deep learning training techniques.

The emergence of foundation models, including language and vision models, has reshaped AI's landscape, offering capabilities across various applications. Deploying and fine-tuning these large models, like GPT-3 and BERT, presents challenges, especially in the current foundation model era. We introduce Emulator-Assisted Tuning (EAT) combined with Parameter-Efficient Fine-Tuning (PEFT) to form Parameter-Efficient Emulator-Assisted Tuning (PEAT). Further, we expand this into federated learning as Federated PEAT (FedPEAT). FedPEAT uses adapters, emulators, and PEFT for federated model tuning, enhancing model privacy and memory efficiency. Adapters adjust pre-trained models, while emulators give a compact representation of original models, addressing both privacy and efficiency. Adaptable to various neural networks, our approach also uses deep reinforcement learning for hyper-parameter optimization. We tested FedPEAT in a unique scenario with a server participating in collaborative federated tuning, showcasing its potential in tackling foundation model challenges.

Machine learning is a powerful tool for extracting valuable information and making various predictions from diverse datasets. Traditional algorithms rely on well-defined input and output variables however, there are scenarios where the distinction between the input and output variables and the underlying, associated (input and output) layers of the model, are unknown. Neural Architecture Search (NAS) and Feature Selection have emerged as promising solutions in such scenarios. This research proposes IDENAS, an Internal Dependency-based Exploration for Neural Architecture Search, integrating NAS with feature selection. The methodology explores internal dependencies in the complete parameter space for classification involving 1D sensor and 2D image data as well. IDENAS employs a modified encoder-decoder model and the Sequential Forward Search (SFS) algorithm, combining input-output configuration search with embedded feature selection. Experimental results demonstrate IDENASs superior performance in comparison to other algorithms, showcasing its effectiveness in model development pipelines and automated machine learning. On average, IDENAS achieved significant modelling improvements, underscoring its significant contribution to advancing the state-of-the-art in neural architecture search and feature selection integration.

Alzheimer's disease (AD) is a prevalent and debilitating neurodegenerative disorder impacting a large aging population. Detecting AD in all its presymptomatic and symptomatic stages is crucial for early intervention and treatment. An active research direction is to explore machine learning methods that harness multimodal data fusion to outperform human inspection of medical scans. However, existing multimodal fusion models have limitations, including redundant computation, complex architecture, and simplistic handling of missing data. Moreover, the preprocessing pipelines of medical scans remain inadequately detailed and are seldom optimized for individual subjects. In this paper, we propose an efficient early-late fusion (ELF) approach, which leverages a convolutional neural network for automated feature extraction and random forests for their competitive performance on small datasets. Additionally, we introduce a robust preprocessing pipeline that adapts to the unique characteristics of individual subjects and makes use of whole brain images rather than slices or patches. Moreover, to tackle the challenge of detecting subtle changes in brain volume, we transform images into the Jacobian domain (JD) to enhance both accuracy and robustness in our classification. Using MRI and CT images from the OASIS-3 dataset, our experiments demonstrate the effectiveness of the ELF approach in classifying AD into four stages with an accuracy of 97.19%.

To process sensor data in the Internet of Things(IoTs), embedded deep learning for 1-dimensional data is an important technique. In the past, CNNs were frequently used because they are simple to optimise for special embedded hardware such as FPGAs. This work proposes a novel LSTM cell optimisation aimed at energy-efficient inference on end devices. Using the traffic speed prediction as a case study, a vanilla LSTM model with the optimised LSTM cell achieves 17534 inferences per second while consuming only 3.8 $\mu$J per inference on the FPGA \textit{XC7S15} from \textit{Spartan-7} family. It achieves at least 5.4$\times$ faster throughput and 1.37$\times$ more energy efficient than existing approaches.

Transformers have achieved great success in machine learning applications. Normalization techniques, such as Layer Normalization (LayerNorm, LN) and Root Mean Square Normalization (RMSNorm), play a critical role in accelerating and stabilizing the training of Transformers. While LayerNorm recenters and rescales input vectors, RMSNorm only rescales the vectors by their RMS value. Despite being more computationally efficient, RMSNorm may compromise the representation ability of Transformers. There is currently no consensus regarding the preferred normalization technique, as some models employ LayerNorm while others utilize RMSNorm, especially in recent large language models. It is challenging to convert Transformers with one normalization to the other type. While there is an ongoing disagreement between the two normalization types, we propose a solution to unify two mainstream Transformer architectures, Pre-LN and Pre-RMSNorm Transformers. By removing the inherent redundant mean information in the main branch of Pre-LN Transformers, we can reduce LayerNorm to RMSNorm, achieving higher efficiency. We further propose the Compressed RMSNorm (CRMSNorm) and Pre-CRMSNorm Transformer based on a lossless compression of the zero-mean vectors. We formally establish the equivalence of Pre-LN, Pre-RMSNorm, and Pre-CRMSNorm Transformer variants in both training and inference. It implies that Pre-LN Transformers can be substituted with Pre-(C)RMSNorm counterparts at almost no cost, offering the same arithmetic functionality along with free efficiency improvement. Experiments demonstrate that we can reduce the training and inference time of Pre-LN Transformers by 1% - 10%.

There is an increasing interest in learning reward functions that model human preferences. However, many frameworks use blackbox learning methods that, while expressive, are difficult to interpret. We propose and evaluate a novel approach for learning expressive and interpretable reward functions from preferences using Differentiable Decision Trees (DDTs). Our experiments across several domains, including CartPole, Visual Gridworld environments and Atari games, provide evidence that that the tree structure of our learned reward function is useful in determining the extent to which the reward function is aligned with human preferences. We provide experimental evidence that reward DDTs can achieve competitive performance when compared with larger capacity deep neural network reward functions. We also observe that the choice between soft and hard (argmax) output of reward DDT reveals a tension between wanting highly shaped rewards to ensure good RL performance, while also wanting simpler, more interpretable rewards.

Ahead-of-time forecasting of the output power of power plants is essential for the stability of the electricity grid and ensuring uninterrupted service. However, forecasting renewable energy sources is difficult due to the chaotic behavior of natural energy sources. This paper presents a new approach to estimate short-term solar irradiance from sky images. The~proposed algorithm extracts features from sky images and use learning-based techniques to estimate the solar irradiance. The~performance of proposed machine learning (ML) algorithm is evaluated using two publicly available datasets of sky images. The~datasets contain over 350,000 images for an interval of 16 years, from 2004 to 2020, with the corresponding global horizontal irradiance (GHI) of each image as the ground truth. Compared to the state-of-the-art computationally heavy algorithms proposed in the literature, our approach achieves competitive results with much less computational complexity for both nowcasting and forecasting up to 4 h ahead of time.

Based on the message-passing paradigm, there has been an amount of research proposing diverse and impressive feature propagation mechanisms to improve the performance of GNNs. However, less focus has been put on feature transformation, another major operation of the message-passing framework. In this paper, we first empirically investigate the performance of the feature transformation operation in several typical GNNs. Unexpectedly, we notice that GNNs do not completely free up the power of the inherent feature transformation operation. By this observation, we propose the Bi-directional Knowledge Transfer (BiKT), a plug-and-play approach to unleash the potential of the feature transformation operations without modifying the original architecture. Taking the feature transformation operation as a derived representation learning model that shares parameters with the original GNN, the direct prediction by this model provides a topological-agnostic knowledge feedback that can further instruct the learning of GNN and the feature transformations therein. On this basis, BiKT not only allows us to acquire knowledge from both the GNN and its derived model but promotes each other by injecting the knowledge into the other. In addition, a theoretical analysis is further provided to demonstrate that BiKT improves the generalization bound of the GNNs from the perspective of domain adaption. An extensive group of experiments on up to 7 datasets with 5 typical GNNs demonstrates that BiKT brings up to 0.5% - 4% performance gain over the original GNN, which means a boosted GNN is obtained. Meanwhile, the derived model also shows a powerful performance to compete with or even surpass the original GNN, enabling us to flexibly apply it independently to some other specific downstream tasks.

Recently, unsupervised adversarial training (AT) has been highlighted as a means of achieving robustness in models without any label information. Previous studies in unsupervised AT have mostly focused on implementing self-supervised learning (SSL) frameworks, which maximize the instance-wise classification loss to generate adversarial examples. However, we observe that simply maximizing the self-supervised training loss with an untargeted adversarial attack often results in generating ineffective adversaries that may not help improve the robustness of the trained model, especially for non-contrastive SSL frameworks without negative examples. To tackle this problem, we propose a novel positive mining for targeted adversarial attack to generate effective adversaries for adversarial SSL frameworks. Specifically, we introduce an algorithm that selects the most confusing yet similar target example for a given instance based on entropy and similarity, and subsequently perturbs the given instance towards the selected target. Our method demonstrates significant enhancements in robustness when applied to non-contrastive SSL frameworks, and less but consistent robustness improvements with contrastive SSL frameworks, on the benchmark datasets.

We introduce a novel grid-independent model for learning partial differential equations (PDEs) from noisy and partial observations on irregular spatiotemporal grids. We propose a space-time continuous latent neural PDE model with an efficient probabilistic framework and a novel encoder design for improved data efficiency and grid independence. The latent state dynamics are governed by a PDE model that combines the collocation method and the method of lines. We employ amortized variational inference for approximate posterior estimation and utilize a multiple shooting technique for enhanced training speed and stability. Our model demonstrates state-of-the-art performance on complex synthetic and real-world datasets, overcoming limitations of previous approaches and effectively handling partially-observed data. The proposed model outperforms recent methods, showing its potential to advance data-driven PDE modeling and enabling robust, grid-independent modeling of complex partially-observed dynamic processes.

In this paper we address the problem of learning and backtesting inventory control policies in the presence of general arrival dynamics -- which we term as a quantity-over-time arrivals model (QOT). We also allow for order quantities to be modified as a post-processing step to meet vendor constraints such as order minimum and batch size constraints -- a common practice in real supply chains. To the best of our knowledge this is the first work to handle either arbitrary arrival dynamics or an arbitrary downstream post-processing of order quantities. Building upon recent work (Madeka et al., 2022) we similarly formulate the periodic review inventory control problem as an exogenous decision process, where most of the state is outside the control of the agent. Madeka et al. (2022) show how to construct a simulator that replays historic data to solve this class of problem. In our case, we incorporate a deep generative model for the arrivals process as part of the history replay. By formulating the problem as an exogenous decision process, we can apply results from Madeka et al. (2022) to obtain a reduction to supervised learning. Finally, we show via simulation studies that this approach yields statistically significant improvements in profitability over production baselines. Using data from an ongoing real-world A/B test, we show that Gen-QOT generalizes well to off-policy data.

Graph neural networks are prominent models for representation learning over graph-structured data. While the capabilities and limitations of these models are well-understood for simple graphs, our understanding remains incomplete in the context of knowledge graphs. Our goal is to provide a systematic understanding of the landscape of graph neural networks for knowledge graphs pertaining to the prominent task of link prediction. Our analysis entails a unifying perspective on seemingly unrelated models and unlocks a series of other models. The expressive power of various models is characterized via a corresponding relational Weisfeiler-Leman algorithm. This analysis is extended to provide a precise logical characterization of the class of functions captured by a class of graph neural networks. The theoretical findings presented in this paper explain the benefits of some widely employed practical design choices, which are validated empirically.

Association rule mining techniques can generate a large volume of sequential data when implemented on transactional databases. Extracting insights from a large set of association rules has been found to be a challenging process. When examining a ruleset, the fundamental question is how to summarise and represent meaningful mined knowledge efficiently. Many algorithms and strategies have been developed to address issue of knowledge extraction; however, the effectiveness of this process can be limited by the data structures. A better data structure can sufficiently affect the speed of the knowledge extraction process. This paper proposes a novel data structure, called the Trie of rules, for storing a ruleset that is generated by association rule mining. The resulting data structure is a prefix-tree graph structure made of pre-mined rules. This graph stores the rules as paths within the prefix-tree in a way that similar rules overlay each other. Each node in the tree represents a rule where a consequent is this node, and an antecedent is a path from this node to the root of the tree. The evaluation showed that the proposed representation technique is promising. It compresses a ruleset with almost no data loss and benefits in terms of time for basic operations such as searching for a specific rule and sorting, which is the base for many knowledge discovery methods. Moreover, our method demonstrated a significant improvement in traversing time, achieving an 8-fold increase compared to traditional data structures.

Since distribution shifts are likely to occur during test-time and can drastically decrease the model's performance, online test-time adaptation (TTA) continues to update the model after deployment, leveraging the current test data. Clearly, a method proposed for online TTA has to perform well for all kinds of environmental conditions. By introducing the variable factors domain non-stationarity and temporal correlation, we first unfold all practically relevant settings and define the entity as universal TTA. We want to highlight that this is the first work that covers such a broad spectrum, which is indispensable for the use in practice. To tackle the problem of universal TTA, we identify and highlight several challenges a self-training based method has to deal with: 1) model bias and the occurrence of trivial solutions when performing entropy minimization on varying sequence lengths with and without multiple domain shifts, 2) loss of generalization which exacerbates the adaptation to multiple domain shifts and the occurrence of catastrophic forgetting, and 3) performance degradation due to shifts in class prior. To prevent the model from becoming biased, we leverage a dataset and model-agnostic certainty and diversity weighting. In order to maintain generalization and prevent catastrophic forgetting, we propose to continually weight-average the source and adapted model. To compensate for disparities in the class prior during test-time, we propose an adaptive prior correction scheme that reweights the model's predictions. We evaluate our approach, named ROID, on a wide range of settings, datasets, and models, setting new standards in the field of universal TTA. Code is available at: https://github.com/mariodoebler/test-time-adaptation

In the context of a voice assistant system, steering refers to the phenomenon in which a user issues a follow-up command attempting to direct or clarify a previous turn. We propose STEER, a steering detection model that predicts whether a follow-up turn is a user's attempt to steer the previous command. Constructing a training dataset for steering use cases poses challenges due to the cold-start problem. To overcome this, we developed heuristic rules to sample opt-in usage data, approximating positive and negative samples without any annotation. Our experimental results show promising performance in identifying steering intent, with over 95% accuracy on our sampled data. Moreover, STEER, in conjunction with our sampling strategy, aligns effectively with real-world steering scenarios, as evidenced by its strong zero-shot performance on a human-graded evaluation set. In addition to relying solely on user transcripts as input, we introduce STEER+, an enhanced version of the model. STEER+ utilizes a semantic parse tree to provide more context on out-of-vocabulary words, such as named entities that often occur at the sentence boundary. This further improves model performance, reducing error rate in domains where entities frequently appear, such as messaging. Lastly, we present a data analysis that highlights the improvement in user experience when voice assistants support steering use cases.

Accurate teeth segmentation and orientation are fundamental in modern oral healthcare, enabling precise diagnosis, treatment planning, and dental implant design. In this study, we present a comprehensive approach to teeth segmentation and orientation from panoramic X-ray images, leveraging deep learning techniques. We build our model based on FUSegNet, a popular model originally developed for wound segmentation, and introduce modifications by incorporating grid-based attention gates into the skip connections. We introduce oriented bounding box (OBB) generation through principal component analysis (PCA) for precise tooth orientation estimation. Evaluating our approach on the publicly available DNS dataset, comprising 543 panoramic X-ray images, we achieve the highest Intersection-over-Union (IoU) score of 82.43% and Dice Similarity Coefficient (DSC) score of 90.37% among compared models in teeth instance segmentation. In OBB analysis, we obtain the Rotated IoU (RIoU) score of 82.82%. We also conduct detailed analyses of individual tooth labels and categorical performance, shedding light on strengths and weaknesses. The proposed model's accuracy and versatility offer promising prospects for improving dental diagnoses, treatment planning, and personalized healthcare in the oral domain. Our generated OBB coordinates and codes are available at https://github.com/mrinal054/Instance_teeth_segmentation.

The Backpropagation algorithm has often been criticised for its lack of biological realism. In an attempt to find a more biologically plausible alternative, the recently introduced Forward-Forward algorithm replaces the forward and backward passes of Backpropagation with two forward passes. In this work, we show that the internal representations obtained by the Forward-Forward algorithm can organise into category-specific ensembles exhibiting high sparsity - i.e. composed of an extremely low number of active units. This situation is reminiscent of what has been observed in cortical sensory areas, where neuronal ensembles are suggested to serve as the functional building blocks for perception and action. Interestingly, while this sparse pattern does not typically arise in models trained with standard Backpropagation, it can emerge in networks trained with Backpropagation on the same objective proposed for the Forward-Forward algorithm. These results suggest that the learning procedure proposed by Forward-Forward may be superior to Backpropagation in modelling learning in the cortex, even when a backward pass is used.

Motivated by the challenge of nonstationarity in sequential decision making, we study Online Convex Optimization (OCO) under the coupling of two problem structures: the domain is unbounded, and the comparator sequence $u_1,\ldots,u_T$ is arbitrarily time-varying. As no algorithm can guarantee low regret simultaneously against all comparator sequences, handling this setting requires moving from minimax optimality to comparator adaptivity. That is, sensible regret bounds should depend on certain complexity measures of the comparator relative to one's prior knowledge. This paper achieves a new type of these adaptive regret bounds via a sparse coding framework. The complexity of the comparator is measured by its energy and its sparsity on a user-specified dictionary, which offers considerable versatility. Equipped with a wavelet dictionary for example, our framework improves the state-of-the-art bound (Jacobsen & Cutkosky, 2022) by adapting to both ($i$) the magnitude of the comparator average $||\bar u||=||\sum_{t=1}^Tu_t/T||$, rather than the maximum $\max_t||u_t||$; and ($ii$) the comparator variability $\sum_{t=1}^T||u_t-\bar u||$, rather than the uncentered sum $\sum_{t=1}^T||u_t||$. Furthermore, our analysis is simpler due to decoupling function approximation from regret minimization.

Noisy marginals are a common form of confidentiality-protecting data release and are useful for many downstream tasks such as contingency table analysis, construction of Bayesian networks, and even synthetic data generation. Privacy mechanisms that provide unbiased noisy answers to linear queries (such as marginals) are known as matrix mechanisms. We propose ResidualPlanner, a matrix mechanism for marginals with Gaussian noise that is both optimal and scalable. ResidualPlanner can optimize for many loss functions that can be written as a convex function of marginal variances (prior work was restricted to just one predefined objective function). ResidualPlanner can optimize the accuracy of marginals in large scale settings in seconds, even when the previous state of the art (HDMM) runs out of memory. It even runs on datasets with 100 attributes in a couple of minutes. Furthermore ResidualPlanner can efficiently compute variance/covariance values for each marginal (prior methods quickly run out of memory, even for relatively small datasets).

Offline reinforcement learning (RL) aims to learn optimal policies from offline datasets, where the parameterization of policies is crucial but often overlooked. Recently, Diffsuion-QL significantly boosts the performance of offline RL by representing a policy with a diffusion model, whose success relies on a parametrized Markov Chain with hundreds of steps for sampling. However, Diffusion-QL suffers from two critical limitations. 1) It is computationally inefficient to forward and backward through the whole Markov chain during training. 2) It is incompatible with maximum likelihood-based RL algorithms (e.g., policy gradient methods) as the likelihood of diffusion models is intractable. Therefore, we propose efficient diffusion policy (EDP) to overcome these two challenges. EDP approximately constructs actions from corrupted ones at training to avoid running the sampling chain. We conduct extensive experiments on the D4RL benchmark. The results show that EDP can reduce the diffusion policy training time from 5 days to 5 hours on gym-locomotion tasks. Moreover, we show that EDP is compatible with various offline RL algorithms (TD3, CRR, and IQL) and achieves new state-of-the-art on D4RL by large margins over previous methods. Our code is available at https://github.com/sail-sg/edp.

We study multitask online learning in a setting where agents can only exchange information with their neighbors on an arbitrary communication network. We introduce $\texttt{MT-CO}_2\texttt{OL}$, a decentralized algorithm for this setting whose regret depends on the interplay between the task similarities and the network structure. Our analysis shows that the regret of $\texttt{MT-CO}_2\texttt{OL}$ is never worse (up to constants) than the bound obtained when agents do not share information. On the other hand, our bounds significantly improve when neighboring agents operate on similar tasks. In addition, we prove that our algorithm can be made differentially private with a negligible impact on the regret when the losses are linear. Finally, we provide experimental support for our theory.

In this paper, we propose the first sketch-and-project Newton method with fast $\mathcal O(k^{-2})$ global convergence rate for self-concordant functions. Our method, SGN, can be viewed in three ways: i) as a sketch-and-project algorithm projecting updates of Newton method, ii) as a cubically regularized Newton ethod in sketched subspaces, and iii) as a damped Newton method in sketched subspaces. SGN inherits best of all three worlds: cheap iteration costs of sketch-and-project methods, state-of-the-art $\mathcal O(k^{-2})$ global convergence rate of full-rank Newton-like methods and the algorithm simplicity of damped Newton methods. Finally, we demonstrate its comparable empirical performance to baseline algorithms.

Graph generative model evaluation necessitates understanding differences between graphs on the distributional level. This entails being able to harness salient attributes of graphs in an efficient manner. Curvature constitutes one such property that has recently proved its utility in characterising graphs. Its expressive properties, stability, and practical utility in model evaluation remain largely unexplored, however. We combine graph curvature descriptors with emerging methods from topological data analysis to obtain robust, expressive descriptors for evaluating graph generative models.

We present a novel methodology aimed at optimizing the application of frozen large language models (LLMs) for resource-intensive vision-language (VL) pre-training. The current paradigm uses visual features as prompts to guide language models, with a focus on determining the most relevant visual features for corresponding text. Our approach diverges by concentrating on the language component, specifically identifying the optimal prompts to align with visual features. We introduce the Prompt-Transformer (P-Former), a model that predicts these ideal prompts, which is trained exclusively on linguistic data, bypassing the need for image-text pairings. This strategy subtly bifurcates the end-to-end VL training process into an additional, separate stage. Our experiments reveal that our framework significantly enhances the performance of a robust image-to-text baseline (BLIP-2), and effectively narrows the performance gap between models trained with either 4M or 129M image-text pairs. Importantly, our framework is modality-agnostic and flexible in terms of architectural design, as validated by its successful application in a video learning task using varied base modules. The code will be made available at https://github.com/yiren-jian/BLIText.

In applying reinforcement learning (RL) to high-stakes domains, quantitative and qualitative evaluation using observational data can help practitioners understand the generalization performance of new policies. However, this type of off-policy evaluation (OPE) is inherently limited since offline data may not reflect the distribution shifts resulting from the application of new policies. On the other hand, online evaluation by collecting rollouts according to the new policy is often infeasible, as deploying new policies in these domains can be unsafe. In this work, we propose a semi-offline evaluation framework as an intermediate step between offline and online evaluation, where human users provide annotations of unobserved counterfactual trajectories. While tempting to simply augment existing data with such annotations, we show that this naive approach can lead to biased results. Instead, we design a new family of OPE estimators based on importance sampling (IS) and a novel weighting scheme that incorporate counterfactual annotations without introducing additional bias. We analyze the theoretical properties of our approach, showing its potential to reduce both bias and variance compared to standard IS estimators. Our analyses reveal important practical considerations for handling biased, noisy, or missing annotations. In a series of proof-of-concept experiments involving bandits and a healthcare-inspired simulator, we demonstrate that our approach outperforms purely offline IS estimators and is robust to imperfect annotations. Our framework, combined with principled human-centered design of annotation solicitation, can enable the application of RL in high-stakes domains.

Forming accurate memory of sequential stimuli is a fundamental function of biological agents. However, the computational mechanism underlying sequential memory in the brain remains unclear. Inspired by neuroscience theories and recent successes in applying predictive coding (PC) to \emph{static} memory tasks, in this work we propose a novel PC-based model for \emph{sequential} memory, called \emph{temporal predictive coding} (tPC). We show that our tPC models can memorize and retrieve sequential inputs accurately with a biologically plausible neural implementation. Importantly, our analytical study reveals that tPC can be viewed as a classical Asymmetric Hopfield Network (AHN) with an implicit statistical whitening process, which leads to more stable performance in sequential memory tasks of structured inputs. Moreover, we find that tPC exhibits properties consistent with behavioral observations and theories in neuroscience, thereby strengthening its biological relevance. Our work establishes a possible computational mechanism underlying sequential memory in the brain that can also be theoretically interpreted using existing memory model frameworks.

Federated learning, a decentralized approach to machine learning, faces significant challenges such as extensive communication overheads, slow convergence, and unstable improvements. These challenges primarily stem from the gradient variance due to heterogeneous client data distributions. To address this, we introduce a novel Networked Control Variates (FedNCV) framework for Federated Learning. We adopt the REINFORCE Leave-One-Out (RLOO) as a fundamental control variate unit in the FedNCV framework, implemented at both client and server levels. At the client level, the RLOO control variate is employed to optimize local gradient updates, mitigating the variance introduced by data samples. Once relayed to the server, the RLOO-based estimator further provides an unbiased and low-variance aggregated gradient, leading to robust global updates. This dual-side application is formalized as a linear combination of composite control variates. We provide a mathematical expression capturing this integration of double control variates within FedNCV and present three theoretical results with corresponding proofs. This unique dual structure equips FedNCV to address data heterogeneity and scalability issues, thus potentially paving the way for large-scale applications. Moreover, we tested FedNCV on six diverse datasets under a Dirichlet distribution with {\alpha} = 0.1, and benchmarked its performance against six SOTA methods, demonstrating its superiority.

In this paper, we critically examine the prevalent practice of using additive mixtures of Mat\'ern kernels in single-output Gaussian process (GP) models and explore the properties of multiplicative mixtures of Mat\'ern kernels for multi-output GP models. For the single-output case, we derive a series of theoretical results showing that the smoothness of a mixture of Mat\'ern kernels is determined by the least smooth component and that a GP with such a kernel is effectively equivalent to the least smooth kernel component. Furthermore, we demonstrate that none of the mixing weights or parameters within individual kernel components are identifiable. We then turn our attention to multi-output GP models and analyze the identifiability of the covariance matrix $A$ in the multiplicative kernel $K(x,y) = AK_0(x,y)$, where $K_0$ is a standard single output kernel such as Mat\'ern. We show that $A$ is identifiable up to a multiplicative constant, suggesting that multiplicative mixtures are well suited for multi-output tasks. Our findings are supported by extensive simulations and real applications for both single- and multi-output settings. This work provides insight into kernel selection and interpretation for GP models, emphasizing the importance of choosing appropriate kernel structures for different tasks.

Accurately forecasting solar power generation is crucial in the global progression towards sustainable energy systems. In this study, we conduct a meticulous comparison between Quantum Long Short-Term Memory (QLSTM) and classical Long Short-Term Memory (LSTM) models for solar power production forecasting. Our controlled experiments reveal promising advantages of QLSTMs, including accelerated training convergence and substantially reduced test loss within the initial epoch compared to classical LSTMs. These empirical findings demonstrate QLSTM's potential to swiftly assimilate complex time series relationships, enabled by quantum phenomena like superposition. However, realizing QLSTM's full capabilities necessitates further research into model validation across diverse conditions, systematic hyperparameter optimization, hardware noise resilience, and applications to correlated renewable forecasting problems. With continued progress, quantum machine learning can offer a paradigm shift in renewable energy time series prediction. This pioneering work provides initial evidence substantiating quantum advantages over classical LSTM, while acknowledging present limitations. Through rigorous benchmarking grounded in real-world data, our study elucidates a promising trajectory for quantum learning in renewable forecasting. Additional research and development can further actualize this potential to achieve unprecedented accuracy and reliability in predicting solar power generation worldwide.

In this study, we leverage a deep learning-based method for the automatic diagnosis of schizophrenia using EEG brain recordings. This approach utilizes generative data augmentation, a powerful technique that enhances the accuracy of the diagnosis. To enable the utilization of time-frequency features, spectrograms were extracted from the raw signals. After exploring several neural network architectural setups, a proper convolutional neural network (CNN) was used for the initial diagnosis. Subsequently, using Wasserstein GAN with Gradient Penalty (WGAN-GP) and Variational Autoencoder (VAE), two different synthetic datasets were generated in order to augment the initial dataset and address the over-fitting issue. The augmented dataset using VAE achieved a 3.0\% improvement in accuracy reaching up to 99.0\% and yielded a lower loss value as well as a faster convergence. Finally, we addressed the lack of trust in black-box models using the Local Interpretable Model-agnostic Explanations (LIME) algorithm to determine the most important superpixels (frequencies) in the diagnosis process.

The global need for effective disease diagnosis remains substantial, given the complexities of various disease mechanisms and diverse patient symptoms. To tackle these challenges, researchers, physicians, and patients are turning to machine learning (ML), an artificial intelligence (AI) discipline, to develop solutions. By leveraging sophisticated ML and AI methods, healthcare stakeholders gain enhanced diagnostic and treatment capabilities. However, there is a scarcity of research focused on ML algorithms for enhancing the accuracy and computational efficiency. This research investigates the capacity of machine learning algorithms to improve the transmission of heart rate data in time series healthcare metrics, concentrating particularly on optimizing accuracy and efficiency. By exploring various ML algorithms used in healthcare applications, the review presents the latest trends and approaches in ML-based disease diagnosis (MLBDD). The factors under consideration include the algorithm utilized, the types of diseases targeted, the data types employed, the applications, and the evaluation metrics. This review aims to shed light on the prospects of ML in healthcare, particularly in disease diagnosis. By analyzing the current literature, the study provides insights into state-of-the-art methodologies and their performance metrics.

Deep reinforcement learning agents for continuous control are known to exhibit significant instability in their performance over time. In this work, we provide a fresh perspective on these behaviors by studying the return landscape: the mapping between a policy and a return. We find that popular algorithms traverse noisy neighborhoods of this landscape, in which a single update to the policy parameters leads to a wide range of returns. By taking a distributional view of these returns, we map the landscape, characterizing failure-prone regions of policy space and revealing a hidden dimension of policy quality. We show that the landscape exhibits surprising structure by finding simple paths in parameter space which improve the stability of a policy. To conclude, we develop a distribution-aware procedure which finds such paths, navigating away from noisy neighborhoods in order to improve the robustness of a policy. Taken together, our results provide new insight into the optimization, evaluation, and design of agents.

A fundamental problem of causal discovery is cause-effect inference, learning the correct causal direction between two random variables. Significant progress has been made through modelling the effect as a function of its cause and a noise term, which allows us to leverage assumptions about the generating function class. The recently introduced heteroscedastic location-scale noise functional models (LSNMs) combine expressive power with identifiability guarantees. LSNM model selection based on maximizing likelihood achieves state-of-the-art accuracy, when the noise distributions are correctly specified. However, through an extensive empirical evaluation, we demonstrate that the accuracy deteriorates sharply when the form of the noise distribution is misspecified by the user. Our analysis shows that the failure occurs mainly when the conditional variance in the anti-causal direction is smaller than that in the causal direction. As an alternative, we find that causal model selection through residual independence testing is much more robust to noise misspecification and misleading conditional variance.

Many recent successful off-policy multi-agent reinforcement learning (MARL) algorithms for cooperative partially observable environments focus on finding factorized value functions, leading to convoluted network structures. Building on the structure of independent Q-learners, our LAN algorithm takes a radically different approach, leveraging a dueling architecture to learn for each agent a decentralized best-response policies via individual advantage functions. The learning is stabilized by a centralized critic whose primary objective is to reduce the moving target problem of the individual advantages. The critic, whose network's size is independent of the number of agents, is cast aside after learning. Evaluation on the StarCraft II multi-agent challenge benchmark shows that LAN reaches state-of-the-art performance and is highly scalable with respect to the number of agents, opening up a promising alternative direction for MARL research.

We consider offline policy optimization (OPO) in contextual bandits, where one is given a fixed dataset of logged interactions. While pessimistic regularizers are typically used to mitigate distribution shift, prior implementations thereof are either specialized or computationally inefficient. We present the first general oracle-efficient algorithm for pessimistic OPO: it reduces to supervised learning, leading to broad applicability. We obtain statistical guarantees analogous to those for prior pessimistic approaches. We instantiate our approach for both discrete and continuous actions and perform experiments in both settings, showing advantage over unregularized OPO across a wide range of configurations.

We present a surprisingly simple and efficient method for self-supervision of 3D backbone on automotive Lidar point clouds. We design a contrastive loss between features of Lidar scans captured in the same scene. Several such approaches have been proposed in the literature from PointConstrast, which uses a contrast at the level of points, to the state-of-the-art TARL, which uses a contrast at the level of segments, roughly corresponding to objects. While the former enjoys a great simplicity of implementation, it is surpassed by the latter, which however requires a costly pre-processing. In BEVContrast, we define our contrast at the level of 2D cells in the Bird's Eye View plane. Resulting cell-level representations offer a good trade-off between the point-level representations exploited in PointContrast and segment-level representations exploited in TARL: we retain the simplicity of PointContrast (cell representations are cheap to compute) while surpassing the performance of TARL in downstream semantic segmentation.

As the peak of the solar cycle approaches in 2025 and the ability of a single geomagnetic storm to significantly alter the orbit of Resident Space Objects (RSOs), techniques for atmospheric density forecasting are vital for space situational awareness. While linear data-driven methods, such as dynamic mode decomposition with control (DMDc), have been used previously for forecasting atmospheric density, deep learning-based forecasting has the ability to capture nonlinearities in data. By learning multiple layer weights from historical atmospheric density data, long-term dependencies in the dataset are captured in the mapping between the current atmospheric density state and control input to the atmospheric density state at the next timestep. This work improves upon previous linear propagation methods for atmospheric density forecasting, by developing a nonlinear transformer-based architecture for atmospheric density forecasting. Empirical NRLMSISE-00 and JB2008, as well as physics-based TIEGCM atmospheric density models are compared for forecasting with DMDc and with the transformer-based propagator.  ( 2 min )

Real-world natural language processing systems need to be robust to human adversaries. Collecting examples of human adversaries for training is an effective but expensive solution. On the other hand, training on synthetic attacks with small perturbations - such as word-substitution - does not actually improve robustness to human adversaries. In this paper, we propose an adversarial training framework that uses limited human adversarial examples to generate more useful adversarial examples at scale. We demonstrate the advantages of this system on the ANLI and hate speech detection benchmark datasets - both collected via an iterative, adversarial human-and-model-in-the-loop procedure. Compared to training only on observed human attacks, also training on our synthetic adversarial examples improves model robustness to future rounds. In ANLI, we see accuracy gains on the current set of attacks (44.1%$\,\to\,$50.1%) and on two future unseen rounds of human generated attacks (32.5%$\,\to\,$43.4%, and 29.4%$\,\to\,$40.2%). In hate speech detection, we see AUC gains on current attacks (0.76 $\to$ 0.84) and a future round (0.77 $\to$ 0.79). Attacks from methods that do not learn the distribution of existing human adversaries, meanwhile, degrade robustness.  ( 2 min )

Continuous latent variables (LVs) are a key ingredient of many generative models, as they allow modelling expressive mixtures with an uncountable number of components. In contrast, probabilistic circuits (PCs) are hierarchical discrete mixtures represented as computational graphs composed of input, sum and product units. Unlike continuous LV models, PCs provide tractable inference but are limited to discrete LVs with categorical (i.e. unordered) states. We bridge these model classes by introducing probabilistic integral circuits (PICs), a new language of computational graphs that extends PCs with integral units representing continuous LVs. In the first place, PICs are symbolic computational graphs and are fully tractable in simple cases where analytical integration is possible. In practice, we parameterise PICs with light-weight neural nets delivering an intractable hierarchical continuous mixture that can be approximated arbitrarily well with large PCs using numerical quadrature. On several distribution estimation benchmarks, we show that such PIC-approximating PCs systematically outperform PCs commonly learned via expectation-maximization or SGD.  ( 2 min )

This research underscores the efficacy of Fourier topological optimization in refining MRI imagery, thereby bolstering the classification precision of Alzheimer's Disease through convolutional neural networks. Recognizing that MRI scans are indispensable for neurological assessments, but frequently grapple with issues like blurriness and contrast irregularities, the deployment of Fourier topological optimization offered enhanced delineation of brain structures, ameliorated noise, and superior contrast. The applied techniques prioritized boundary enhancement, contrast and brightness adjustments, and overall image lucidity. Employing CNN architectures VGG16, ResNet50, InceptionV3, and Xception, the post-optimization analysis revealed a marked elevation in performance. Conclusively, the amalgamation of Fourier topological optimization with CNNs delineates a promising trajectory for the nuanced classification of Alzheimer's Disease, portending a transformative impact on its diagnostic paradigms.  ( 2 min )

Flexible models for probability distributions are an essential ingredient in many machine learning tasks. We develop and investigate a new class of probability distributions, which we call a Squared Neural Family (SNEFY), formed by squaring the 2-norm of a neural network and normalising it with respect to a base measure. Following the reasoning similar to the well established connections between infinitely wide neural networks and Gaussian processes, we show that SNEFYs admit closed form normalising constants in many cases of interest, thereby resulting in flexible yet fully tractable density models. SNEFYs strictly generalise classical exponential families, are closed under conditioning, and have tractable marginal distributions. Their utility is illustrated on a variety of density estimation, conditional density estimation, and density estimation with missing data tasks.

In robotics and artificial intelligence, the integration of tactile processing is becoming increasingly pivotal, especially in learning to execute intricate tasks like alignment and insertion. However, existing works focusing on tactile methods for insertion tasks predominantly rely on robot teleoperation data and reinforcement learning, which do not utilize the rich insights provided by human's control strategy guided by tactile feedback. For utilizing human sensations, methodologies related to learning from humans predominantly leverage visual feedback, often overlooking the invaluable tactile feedback that humans inherently employ to finish complex manipulations. Addressing this gap, we introduce "MimicTouch", a novel framework that mimics human's tactile-guided control strategy. In this framework, we initially collect multi-modal tactile datasets from human demonstrators, incorporating human tactile-guided control strategies for task completion. The subsequent step involves instructing robots through imitation learning using multi-modal sensor data and retargeted human motions. To further mitigate the embodiment gap between humans and robots, we employ online residual reinforcement learning on the physical robot. Through comprehensive experiments, we validate the safety of MimicTouch in transferring a latent policy learned through imitation learning from human to robot. This ongoing work will pave the way for a broader spectrum of tactile-guided robotic applications.  ( 2 min )

We aim to produce a smaller language model that is aligned to user intent. Previous research has shown that applying distilled supervised fine-tuning (dSFT) on larger models significantly improves task accuracy; however, these models are unaligned, i.e. they do not respond well to natural prompts. To distill this property, we experiment with the use of preference data from AI Feedback (AIF). Starting from a dataset of outputs ranked by a teacher model, we apply distilled direct preference optimization (dDPO) to learn a chat model with significantly improved intent alignment. The approach requires only a few hours of training without any additional sampling during fine-tuning. The final result, Zephyr-7B, sets the state-of-the-art on chat benchmarks for 7B parameter models, and requires no human annotation. In particular, results on MT-Bench show that Zephyr-7B surpasses Llama2-Chat-70B, the best open-access RLHF-based model. Code, models, data, and tutorials for the system are available at https://github.com/huggingface/alignment-handbook.  ( 2 min )

As large language models (LLMs) are widely adopted, new safety issues and policies emerge, to which existing safety classifiers do not generalize well. If we have only observed a few examples of violations of a new safety rule, how can we build a classifier to detect violations? In this paper, we study the novel setting of domain-generalized few-shot learning for LLM-based text safety classifiers. Unlike prior few-shot work, these new safety issues can be hard to uncover and we do not get to choose the few examples. We demonstrate that existing few-shot techniques do not perform well in this setting, and rather we propose to do parameter-efficient fine-tuning (PEFT) combined with augmenting training data based on similar examples in prior existing rules. We empirically show that our approach of similarity-based data-augmentation + prompt-tuning (DAPT) consistently outperforms baselines that either do not rely on data augmentation or on PEFT by 7-17% F1 score in the Social Chemistry moral judgement and 9-13% AUC in the Toxicity detection tasks, even when the new rule is loosely correlated with existing ones.  ( 2 min )

Accurate estimation of conditional average treatment effects (CATE) is at the core of personalized decision making. While there is a plethora of models for CATE estimation, model selection is a nontrivial task, due to the fundamental problem of causal inference. Recent empirical work provides evidence in favor of proxy loss metrics with double robust properties and in favor of model ensembling. However, theoretical understanding is lacking. Direct application of prior theoretical work leads to suboptimal oracle model selection rates due to the non-convexity of the model selection problem. We provide regret rates for the major existing CATE ensembling approaches and propose a new CATE model ensembling approach based on Q-aggregation using the doubly robust loss. Our main result shows that causal Q-aggregation achieves statistically optimal oracle model selection regret rates of $\frac{\log(M)}{n}$ (with $M$ models and $n$ samples), with the addition of higher-order estimation error terms related to products of errors in the nuisance functions. Crucially, our regret rate does not require that any of the candidate CATE models be close to the truth. We validate our new method on many semi-synthetic datasets and also provide extensions of our work to CATE model selection with instrumental variables and unobserved confounding.  ( 2 min )

Machine learning-based performance models are increasingly being used to build critical job scheduling and application optimization decisions. Traditionally, these models assume that data distribution does not change as more samples are collected over time. However, owing to the complexity and heterogeneity of production HPC systems, they are susceptible to hardware degradation, replacement, and/or software patches, which can lead to drift in the data distribution that can adversely affect the performance models. To this end, we develop continually learning performance models that account for the distribution drift, alleviate catastrophic forgetting, and improve generalizability. Our best model was able to retain accuracy, regardless of having to learn the new distribution of data inflicted by system changes, while demonstrating a 2x improvement in the prediction accuracy of the whole data sequence in comparison to the naive approach.  ( 2 min )

We study the problem of determining the emergent behaviors that are possible given a functionally heterogeneous swarm of robots with limited capabilities. Prior work has considered behavior search for homogeneous swarms and proposed the use of novelty search over either a hand-specified or learned behavior space followed by clustering to return a taxonomy of emergent behaviors to the user. In this paper, we seek to better understand the role of novelty search and the efficacy of using clustering to discover novel emergent behaviors. Through a large set of experiments and ablations, we analyze the effect of representations, evolutionary search, and various clustering methods in the search for novel behaviors in a heterogeneous swarm. Our results indicate that prior methods fail to discover many interesting behaviors and that an iterative human-in-the-loop discovery process discovers more behaviors than random search, swarm chemistry, and automated behavior discovery. The combined discoveries of our experiments uncover 23 emergent behaviors, 18 of which are novel discoveries. To the best of our knowledge, these are the first known emergent behaviors for heterogeneous swarms of computation-free agents. Videos, code, and appendix are available at the project website: https://sites.google.com/view/heterogeneous-bd-methods  ( 2 min )

The pre-training architectures of large language models encompass various types, including autoencoding models, autoregressive models, and encoder-decoder models. We posit that any modality can potentially benefit from a large language model, as long as it undergoes vector quantization to become discrete tokens. Inspired by GLM, we propose a General Point Model (GPM) which seamlessly integrates autoencoding and autoregressive tasks in point cloud transformer. This model is versatile, allowing fine-tuning for downstream point cloud representation tasks, as well as unconditional and conditional generation tasks. GPM enhances masked prediction in autoencoding through various forms of mask padding tasks, leading to improved performance in point cloud understanding. Additionally, GPM demonstrates highly competitive results in unconditional point cloud generation tasks, even exhibiting the potential for conditional generation tasks by modifying the input's conditional information. Compared to models like Point-BERT, MaskPoint and PointMAE, our GPM achieves superior performance in point cloud understanding tasks. Furthermore, the integration of autoregressive and autoencoding within the same transformer underscores its versatility across different downstream tasks.  ( 2 min )

Graph neural networks (GNNs) have become increasingly popular for classification tasks on graph-structured data. Yet, the interplay between graph topology and feature evolution in GNNs is not well understood. In this paper, we focus on node-wise classification, illustrated with community detection on stochastic block model graphs, and explore the feature evolution through the lens of the "Neural Collapse" (NC) phenomenon. When training instance-wise deep classifiers (e.g. for image classification) beyond the zero training error point, NC demonstrates a reduction in the deepest features' within-class variability and an increased alignment of their class means to certain symmetric structures. We start with an empirical study that shows that a decrease in within-class variability is also prevalent in the node-wise classification setting, however, not to the extent observed in the instance-wise case. Then, we theoretically study this distinction. Specifically, we show that even an "optimistic" mathematical model requires that the graphs obey a strict structural condition in order to possess a minimizer with exact collapse. Interestingly, this condition is viable also for heterophilic graphs and relates to recent empirical studies on settings with improved GNNs' generalization. Furthermore, by studying the gradient dynamics of the theoretical model, we provide reasoning for the partial collapse observed empirically. Finally, we present a study on the evolution of within- and between-class feature variability across layers of a well-trained GNN and contrast the behavior with spectral methods.  ( 3 min )

We propose a method to detect model misspecifications in nonlinear causal additive and potentially heteroscedastic noise models. We aim to identify predictor variables for which we can infer the causal effect even in cases of such misspecification. We develop a general framework based on knowledge of the multivariate observational data distribution and we then propose an algorithm for finite sample data, discuss its asymptotic properties, and illustrate its performance on simulated and real data.

We introduce a suite of new particle-based algorithms for sampling on constrained domains which are entirely learning rate free. Our approach leverages coin betting ideas from convex optimisation, and the viewpoint of constrained sampling as a mirrored optimisation problem on the space of probability measures. Based on this viewpoint, we also introduce a unifying framework for several existing constrained sampling algorithms, including mirrored Langevin dynamics and mirrored Stein variational gradient descent. We demonstrate the performance of our algorithms on a range of numerical examples, including sampling from targets on the simplex, sampling with fairness constraints, and constrained sampling problems in post-selection inference. Our results indicate that our algorithms achieve competitive performance with existing constrained sampling methods, without the need to tune any hyperparameters.

Along with recent diffusion models, randomized smoothing has become one of a few tangible approaches that offers adversarial robustness to models at scale, e.g., those of large pre-trained models. Specifically, one can perform randomized smoothing on any classifier via a simple "denoise-and-classify" pipeline, so-called denoised smoothing, given that an accurate denoiser is available - such as diffusion model. In this paper, we present scalable methods to address the current trade-off between certified robustness and accuracy in denoised smoothing. Our key idea is to "selectively" apply smoothing among multiple noise scales, coined multi-scale smoothing, which can be efficiently implemented with a single diffusion model. This approach also suggests a new objective to compare the collective robustness of multi-scale smoothed classifiers, and questions which representation of diffusion model would maximize the objective. To address this, we propose to further fine-tune diffusion model (a) to perform consistent denoising whenever the original image is recoverable, but (b) to generate rather diverse outputs otherwise. Our experiments show that the proposed multi-scale smoothing scheme combined with diffusion fine-tuning enables strong certified robustness available with high noise level while maintaining its accuracy closer to non-smoothed classifiers.  ( 2 min )

In predictive modeling with simulation or machine learning, it is critical to accurately assess the quality of estimated values through output analysis. In recent decades output analysis has become enriched with methods that quantify the impact of input data uncertainty in the model outputs to increase robustness. However, most developments are applicable assuming that the input data adheres to a parametric family of distributions. We propose a unified output analysis framework for simulation and machine learning outputs through the lens of Monte Carlo sampling. This framework provides nonparametric quantification of the variance and bias induced in the outputs with higher-order accuracy. Our new bias-corrected estimation from the model outputs leverages the extension of fast iterative bootstrap sampling and higher-order influence functions. For the scalability of the proposed estimation methods, we devise budget-optimal rules and leverage control variates for variance reduction. Our theoretical and numerical results demonstrate a clear advantage in building more robust confidence intervals from the model outputs with higher coverage probability.  ( 2 min )

We study empirical Bayes estimation in high-dimensional linear regression. To facilitate computationally efficient estimation of the underlying prior, we adopt a variational empirical Bayes approach, introduced originally in Carbonetto and Stephens (2012) and Kim et al. (2022). We establish asymptotic consistency of the nonparametric maximum likelihood estimator (NPMLE) and its (computable) naive mean field variational surrogate under mild assumptions on the design and the prior. Assuming, in addition, that the naive mean field approximation has a dominant optimizer, we develop a computationally efficient approximation to the oracle posterior distribution, and establish its accuracy under the 1-Wasserstein metric. This enables computationally feasible Bayesian inference; e.g., construction of posterior credible intervals with an average coverage guarantee, Bayes optimal estimation for the regression coefficients, estimation of the proportion of non-nulls, etc. Our analysis covers both deterministic and random designs, and accommodates correlations among the features. To the best of our knowledge, this provides the first rigorous nonparametric empirical Bayes method in a high-dimensional regression setting without sparsity.  ( 2 min )

Variable importance assessment has become a crucial step in machine-learning applications when using complex learners, such as deep neural networks, on large-scale data. Removal-based importance assessment is currently the reference approach, particularly when statistical guarantees are sought to justify variable inclusion. It is often implemented with variable permutation schemes. On the flip side, these approaches risk misidentifying unimportant variables as important in the presence of correlations among covariates. Here we develop a systematic approach for studying Conditional Permutation Importance (CPI) that is model agnostic and computationally lean, as well as reusable benchmarks of state-of-the-art variable importance estimators. We show theoretically and empirically that $\textit{CPI}$ overcomes the limitations of standard permutation importance by providing accurate type-I error control. When used with a deep neural network, $\textit{CPI}$ consistently showed top accuracy across benchmarks. An experiment on real-world data analysis in a large-scale medical dataset showed that $\textit{CPI}$ provides a more parsimonious selection of statistically significant variables. Our results suggest that $\textit{CPI}$ can be readily used as drop-in replacement for permutation-based methods.  ( 3 min )

Motivated by the challenge of nonstationarity in sequential decision making, we study Online Convex Optimization (OCO) under the coupling of two problem structures: the domain is unbounded, and the comparator sequence $u_1,\ldots,u_T$ is arbitrarily time-varying. As no algorithm can guarantee low regret simultaneously against all comparator sequences, handling this setting requires moving from minimax optimality to comparator adaptivity. That is, sensible regret bounds should depend on certain complexity measures of the comparator relative to one's prior knowledge. This paper achieves a new type of these adaptive regret bounds via a sparse coding framework. The complexity of the comparator is measured by its energy and its sparsity on a user-specified dictionary, which offers considerable versatility. Equipped with a wavelet dictionary for example, our framework improves the state-of-the-art bound (Jacobsen & Cutkosky, 2022) by adapting to both ($i$) the magnitude of the comparator average $||\bar u||=||\sum_{t=1}^Tu_t/T||$, rather than the maximum $\max_t||u_t||$; and ($ii$) the comparator variability $\sum_{t=1}^T||u_t-\bar u||$, rather than the uncentered sum $\sum_{t=1}^T||u_t||$. Furthermore, our analysis is simpler due to decoupling function approximation from regret minimization.  ( 3 min )

Consider a scenario where we have access to train data with both covariates and outcomes while test data only contains covariates. In this scenario, our primary aim is to predict the missing outcomes of the test data. With this objective in mind, we train parametric regression models under a covariate shift, where covariate distributions are different between the train and test data. For this problem, existing studies have proposed covariate shift adaptation via importance weighting using the density ratio. This approach averages the train data losses, each weighted by an estimated ratio of the covariate densities between the train and test data, to approximate the test-data risk. Although it allows us to obtain a test-data risk minimizer, its performance heavily relies on the accuracy of the density ratio estimation. Moreover, even if the density ratio can be consistently estimated, the estimation errors of the density ratio also yield bias in the estimators of the regression model's parameters of interest. To mitigate these challenges, we introduce a doubly robust estimator for covariate shift adaptation via importance weighting, which incorporates an additional estimator for the regression function. Leveraging double machine learning techniques, our estimator reduces the bias arising from the density ratio estimation errors. We demonstrate the asymptotic distribution of the regression parameter estimator. Notably, our estimator remains consistent if either the density ratio estimator or the regression function is consistent, showcasing its robustness against potential errors in density ratio estimation. Finally, we confirm the soundness of our proposed method via simulation studies.

A fundamental problem of causal discovery is cause-effect inference, learning the correct causal direction between two random variables. Significant progress has been made through modelling the effect as a function of its cause and a noise term, which allows us to leverage assumptions about the generating function class. The recently introduced heteroscedastic location-scale noise functional models (LSNMs) combine expressive power with identifiability guarantees. LSNM model selection based on maximizing likelihood achieves state-of-the-art accuracy, when the noise distributions are correctly specified. However, through an extensive empirical evaluation, we demonstrate that the accuracy deteriorates sharply when the form of the noise distribution is misspecified by the user. Our analysis shows that the failure occurs mainly when the conditional variance in the anti-causal direction is smaller than that in the causal direction. As an alternative, we find that causal model selection through residual independence testing is much more robust to noise misspecification and misleading conditional variance.  ( 2 min )

Cohen et al. (2021) empirically study the evolution of the largest eigenvalue of the loss Hessian, also known as sharpness, along the gradient descent (GD) trajectory and observe the Edge of Stability (EoS) phenomenon. The sharpness increases at the early phase of training (referred to as progressive sharpening), and eventually saturates close to the threshold of $2 / \text{(step size)}$. In this paper, we start by demonstrating through empirical studies that when the EoS phenomenon occurs, different GD trajectories (after a proper reparameterization) align on a specific bifurcation diagram independent of initialization. We then rigorously prove this trajectory alignment phenomenon for a two-layer fully-connected linear network and a single-neuron nonlinear network trained with a single data point. Our trajectory alignment analysis establishes both progressive sharpening and EoS phenomena, encompassing and extending recent findings in the literature.  ( 2 min )

Separating signals from an additive mixture may be an unnecessarily hard problem when one is only interested in specific properties of a given signal. In this work, we tackle simpler "statistical component separation" problems that focus on recovering a predefined set of statistical descriptors of a target signal from a noisy mixture. Assuming access to samples of the noise process, we investigate a method devised to match the statistics of the solution candidate corrupted by noise samples with those of the observed mixture. We first analyze the behavior of this method using simple examples with analytically tractable calculations. Then, we apply it in an image denoising context employing 1) wavelet-based descriptors, 2) ConvNet-based descriptors on astrophysics and ImageNet data. In the case of 1), we show that our method better recovers the descriptors of the target data than a standard denoising method in most situations. Additionally, despite not constructed for this purpose, it performs surprisingly well in terms of peak signal-to-noise ratio on full signal reconstruction. In comparison, representation 2) appears less suitable for image denoising. Finally, we extend this method by introducing a diffusive stepwise algorithm which gives a new perspective to the initial method and leads to promising results for image denoising under specific circumstances.  ( 3 min )

The success of over-parameterized neural networks trained to near-zero training error has caused great interest in the phenomenon of benign overfitting, where estimators are statistically consistent even though they interpolate noisy training data. While benign overfitting in fixed dimension has been established for some learning methods, current literature suggests that for regression with typical kernel methods and wide neural networks, benign overfitting requires a high-dimensional setting where the dimension grows with the sample size. In this paper, we show that the smoothness of the estimators, and not the dimension, is the key: benign overfitting is possible if and only if the estimator's derivatives are large enough. We generalize existing inconsistency results to non-interpolating models and more kernels to show that benign overfitting with moderate derivatives is impossible in fixed dimension. Conversely, we show that rate-optimal benign overfitting is possible for regression with a sequence of spiky-smooth kernels with large derivatives. Using neural tangent kernels, we translate our results to wide neural networks. We prove that while infinite-width networks do not overfit benignly with the ReLU activation, this can be fixed by adding small high-frequency fluctuations to the activation function. Our experiments verify that such neural networks, while overfitting, can indeed generalize well even on low-dimensional data sets.  ( 3 min )

Label embedding is a framework for multiclass classification problems where each label is represented by a distinct vector of some fixed dimension, and training involves matching model output to the vector representing the correct label. While label embedding has been successfully applied in extreme classification and zero-shot learning, and offers both computational and statistical advantages, its theoretical foundations remain poorly understood. This work presents an analysis of label embedding in the context of extreme multiclass classification, where the number of classes $C$ is very large. We present an excess risk bound that reveals a trade-off between computational and statistical efficiency, quantified via the coherence of the embedding matrix. We further show that under the Massart noise condition, the statistical penalty for label embedding vanishes with sufficiently low coherence. Our analysis supports an algorithm that is simple, scalable, and easily parallelizable, and experimental results demonstrate its effectiveness in large-scale applications.  ( 2 min )

In this paper, we consider the uncertainty quantification problem for regression models. Specifically, we consider an individual calibration objective for characterizing the quantiles of the prediction model. While such an objective is well-motivated from downstream tasks such as newsvendor cost, the existing methods have been largely heuristic and lack of statistical guarantee in terms of individual calibration. We show via simple examples that the existing methods focusing on population-level calibration guarantees such as average calibration or sharpness can lead to harmful and unexpected results. We propose simple nonparametric calibration methods that are agnostic of the underlying prediction model and enjoy both computational efficiency and statistical consistency. Our approach enables a better understanding of the possibility of individual calibration, and we establish matching upper and lower bounds for the calibration error of our proposed methods. Technically, our analysis combines the nonparametric analysis with a covering number argument for parametric analysis, which advances the existing theoretical analyses in the literature of nonparametric density estimation and quantile bandit problems. Importantly, the nonparametric perspective sheds new theoretical insights into regression calibration in terms of the curse of dimensionality and reconciles the existing results on the impossibility of individual calibration. To our knowledge, we make the first effort to reach both individual calibration and finite-sample guarantee with minimal assumptions in terms of conformal prediction. Numerical experiments show the advantage of such a simple approach under various metrics, and also under covariates shift. We hope our work provides a simple benchmark and a starting point of theoretical ground for future research on regression calibration.  ( 3 min )

Existing online learning algorithms for adversarial Markov Decision Processes achieve ${O}(\sqrt{T})$ regret after $T$ rounds of interactions even if the loss functions are chosen arbitrarily by an adversary, with the caveat that the transition function has to be fixed. This is because it has been shown that adversarial transition functions make no-regret learning impossible. Despite such impossibility results, in this work, we develop algorithms that can handle both adversarial losses and adversarial transitions, with regret increasing smoothly in the degree of maliciousness of the adversary. More concretely, we first propose an algorithm that enjoys $\widetilde{{O}}(\sqrt{T} + C^{\textsf{P}})$ regret where $C^{\textsf{P}}$ measures how adversarial the transition functions are and can be at most ${O}(T)$. While this algorithm itself requires knowledge of $C^{\textsf{P}}$, we further develop a black-box reduction approach that removes this requirement. Moreover, we also show that further refinements of the algorithm not only maintains the same regret bound, but also simultaneously adapts to easier environments (where losses are generated in a certain stochastically constrained manner as in Jin et al. [2021]) and achieves $\widetilde{{O}}(U + \sqrt{UC^{\textsf{L}}} + C^{\textsf{P}})$ regret, where $U$ is some standard gap-dependent coefficient and $C^{\textsf{L}}$ is the amount of corruption on losses.  ( 3 min )

We study the problem of designing adaptive multi-armed bandit algorithms that perform optimally in both the stochastic setting and the adversarial setting simultaneously (often known as a best-of-both-world guarantee). A line of recent works shows that when configured and analyzed properly, the Follow-the-Regularized-Leader (FTRL) algorithm, originally designed for the adversarial setting, can in fact optimally adapt to the stochastic setting as well. Such results, however, critically rely on an assumption that there exists one unique optimal arm. Recently, Ito (2021) took the first step to remove such an undesirable uniqueness assumption for one particular FTRL algorithm with the $\frac{1}{2}$-Tsallis entropy regularizer. In this work, we significantly improve and generalize this result, showing that uniqueness is unnecessary for FTRL with a broad family of regularizers and a new learning rate schedule. For some regularizers, our regret bounds also improve upon prior results even when uniqueness holds. We further provide an application of our results to the decoupled exploration and exploitation problem, demonstrating that our techniques are broadly applicable.  ( 3 min )

Ill-posed linear inverse problems arise frequently in various applications, from computational photography to medical imaging. A recent line of research exploits Bayesian inference with informative priors to handle the ill-posedness of such problems. Amongst such priors, score-based generative models (SGM) have recently been successfully applied to several different inverse problems. In this study, we exploit the particular structure of the prior defined by the SGM to define a sequence of intermediate linear inverse problems. As the noise level decreases, the posteriors of these inverse problems get closer to the target posterior of the original inverse problem. To sample from this sequence of posteriors, we propose the use of Sequential Monte Carlo (SMC) methods. The proposed algorithm, MCGDiff, is shown to be theoretically grounded and we provide numerical simulations showing that it outperforms competing baselines when dealing with ill-posed inverse problems in a Bayesian setting.  ( 2 min )

Agglomerative hierarchical clustering based on Ordered Weighted Averaging (OWA) operators not only generalises the single, complete, and average linkages, but also includes intercluster distances based on a few nearest or farthest neighbours, trimmed and winsorised means of pairwise point similarities, amongst many others. We explore the relationships between the famous Lance-Williams update formula and the extended OWA-based linkages with weights generated via infinite coefficient sequences. Furthermore, we provide some conditions for the weight generators to guarantee the resulting dendrograms to be free from unaesthetic inversions.  ( 2 min )

Randomized smoothing is a leading approach for constructing classifiers that are certifiably robust against adversarial examples. Existing work on randomized smoothing has focused on classifiers with continuous inputs, such as images, where $\ell_p$-norm bounded adversaries are commonly studied. However, there has been limited work for classifiers with discrete or variable-size inputs, such as for source code, which require different threat models and smoothing mechanisms. In this work, we adapt randomized smoothing for discrete sequence classifiers to provide certified robustness against edit distance-bounded adversaries. Our proposed smoothing mechanism randomized deletion (RS-Del) applies random deletion edits, which are (perhaps surprisingly) sufficient to confer robustness against adversarial deletion, insertion and substitution edits. Our proof of certification deviates from the established Neyman-Pearson approach, which is intractable in our setting, and is instead organized around longest common subsequences. We present a case study on malware detection--a binary classification problem on byte sequences where classifier evasion is a well-established threat model. When applied to the popular MalConv malware detection model, our smoothing mechanism RS-Del achieves a certified accuracy of 91% at an edit distance radius of 128 bytes.  ( 3 min )

We describe a new direct method to estimate bipartite mutual information of a classical spin system based on Monte Carlo sampling enhanced by autoregressive neural networks. It allows studying arbitrary geometries of subsystems and can be generalized to classical field theories. We demonstrate it on the Ising model for four partitionings, including a multiply-connected even-odd division. We show that the area law is satisfied for temperatures away from the critical temperature: the constant term is universal, whereas the proportionality coefficient is different for the even-odd partitioning.  ( 2 min )

We propose a novel and practical privacy notion called $f$-Membership Inference Privacy ($f$-MIP), which explicitly considers the capabilities of realistic adversaries under the membership inference attack threat model. Consequently, $f$-MIP offers interpretable privacy guarantees and improved utility (e.g., better classification accuracy). In particular, we derive a parametric family of $f$-MIP guarantees that we refer to as $\mu$-Gaussian Membership Inference Privacy ($\mu$-GMIP) by theoretically analyzing likelihood ratio-based membership inference attacks on stochastic gradient descent (SGD). Our analysis highlights that models trained with standard SGD already offer an elementary level of MIP. Additionally, we show how $f$-MIP can be amplified by adding noise to gradient updates. Our analysis further yields an analytical membership inference attack that offers two distinct advantages over previous approaches. First, unlike existing state-of-the-art attacks that require training hundreds of shadow models, our attack does not require any shadow model. Second, our analytical attack enables straightforward auditing of our privacy notion $f$-MIP. Finally, we quantify how various hyperparameters (e.g., batch size, number of model parameters) and specific data characteristics determine an attacker's ability to accurately infer a point's membership in the training set. We demonstrate the effectiveness of our method on models trained on vision and tabular datasets.  ( 2 min )

We introduce a transformation framework that can be utilized to develop online algorithms with low $\epsilon$-approximate regret in the random-order model from offline approximation algorithms. We first give a general reduction theorem that transforms an offline approximation algorithm with low average sensitivity to an online algorithm with low $\epsilon$-approximate regret. We then demonstrate that offline approximation algorithms can be transformed into a low-sensitivity version using a coreset construction method. To showcase the versatility of our approach, we apply it to various problems, including online $(k,z)$-clustering, online matrix approximation, and online regression, and successfully achieve polylogarithmic $\epsilon$-approximate regret for each problem. Moreover, we show that in all three cases, our algorithm also enjoys low inconsistency, which may be desired in some online applications.  ( 2 min )

Adversarial examples resulting from instability of current computer vision models are an extremely important topic due to their potential to compromise any application. In this paper we demonstrate that instability is inevitable due to a) symmetries (translational invariance) of the data, b) the categorical nature of the classification task, and c) the fundamental discrepancy of classifying images as objects themselves. The issue is further exacerbated by non-exhaustive labelling of the training data. Therefore we conclude that instability is a necessary result of how the problem of computer vision is currently formulated. While the problem cannot be eliminated, through the analysis of the causes, we have arrived at ways how it can be partially alleviated. These include i) increasing the resolution of images, ii) providing contextual information for the image, iii) exhaustive labelling of training data, and iv) preventing attackers from frequent access to the computer vision system.  ( 2 min )

Flow-based generative models enjoy certain advantages in computing the data generation and the likelihood, and have recently shown competitive empirical performance. Compared to the accumulating theoretical studies on related score-based diffusion models, analysis of flow-based models, which are deterministic in both forward (data-to-noise) and reverse (noise-to-data) directions, remain sparse. In this paper, we provide a theoretical guarantee of generating data distribution by a progressive flow model, the so-called JKO flow model, which implements the Jordan-Kinderleherer-Otto (JKO) scheme in a normalizing flow network. Leveraging the exponential convergence of the proximal gradient descent (GD) in Wasserstein space, we prove the Kullback-Leibler (KL) guarantee of data generation by a JKO flow model to be $O(\varepsilon^2)$ when using $N \lesssim \log (1/\varepsilon)$ many JKO steps ($N$ Residual Blocks in the flow) where $\varepsilon $ is the error in the per-step first-order condition. The assumption on data density is merely a finite second moment, and the theory extends to data distributions without density and when there are inversion errors in the reverse process where we obtain KL-$W_2$ mixed error guarantees. The non-asymptotic convergence rate of the JKO-type $W_2$-proximal GD is proved for a general class of convex objective functionals that includes the KL divergence as a special case, which can be of independent interest.  ( 3 min )

In this study, we propose a novel multi-objective Bayesian optimization (MOBO) method to efficiently identify the Pareto front (PF) defined by risk measures for black-box functions under the presence of input uncertainty (IU). Existing BO methods for Pareto optimization in the presence of IU are risk-specific or without theoretical guarantees, whereas our proposed method addresses general risk measures and has theoretical guarantees. The basic idea of the proposed method is to assume a Gaussian process (GP) model for the black-box function and to construct high-probability bounding boxes for the risk measures using the GP model. Furthermore, in order to reduce the uncertainty of non-dominated bounding boxes, we propose a method of selecting the next evaluation point using a maximin distance defined by the maximum value of a quasi distance based on bounding boxes. As theoretical analysis, we prove that the algorithm can return an arbitrary-accurate solution in a finite number of iterations with high probability, for various risk measures such as Bayes risk, worst-case risk, and value-at-risk. We also give a theoretical analysis that takes into account approximation errors because there exist non-negligible approximation errors (e.g., finite approximation of PFs and sampling-based approximation of bounding boxes) in practice. We confirm that the proposed method outperforms compared with existing methods not only in the setting with IU but also in the setting of ordinary MOBO through numerical experiments.  ( 3 min )

Graph generative model evaluation necessitates understanding differences between graphs on the distributional level. This entails being able to harness salient attributes of graphs in an efficient manner. Curvature constitutes one such property that has recently proved its utility in characterising graphs. Its expressive properties, stability, and practical utility in model evaluation remain largely unexplored, however. We combine graph curvature descriptors with emerging methods from topological data analysis to obtain robust, expressive descriptors for evaluating graph generative models.  ( 2 min )

Generative diffusion models have achieved spectacular performance in many areas of generative modeling. While the fundamental ideas behind these models come from non-equilibrium physics, in this paper we show that many aspects of these models can be understood using the tools of equilibrium statistical mechanics. Using this reformulation, we show that generative diffusion models undergo second-order phase transitions corresponding to symmetry breaking phenomena. We argue that this lead to a form of instability that lies at the heart of their generative capabilities and that can be described by a set of mean field critical exponents. We conclude by analyzing recent work connecting diffusion models and associative memory networks in view of the thermodynamic formulations.  ( 2 min )

Forming accurate memory of sequential stimuli is a fundamental function of biological agents. However, the computational mechanism underlying sequential memory in the brain remains unclear. Inspired by neuroscience theories and recent successes in applying predictive coding (PC) to \emph{static} memory tasks, in this work we propose a novel PC-based model for \emph{sequential} memory, called \emph{temporal predictive coding} (tPC). We show that our tPC models can memorize and retrieve sequential inputs accurately with a biologically plausible neural implementation. Importantly, our analytical study reveals that tPC can be viewed as a classical Asymmetric Hopfield Network (AHN) with an implicit statistical whitening process, which leads to more stable performance in sequential memory tasks of structured inputs. Moreover, we find that tPC exhibits properties consistent with behavioral observations and theories in neuroscience, thereby strengthening its biological relevance. Our work establishes a possible computational mechanism underlying sequential memory in the brain that can also be theoretically interpreted using existing memory model frameworks.  ( 2 min )

We consider contextual bandit problems with knapsacks [CBwK], a problem where at each round, a scalar reward is obtained and vector-valued costs are suffered. The learner aims to maximize the cumulative rewards while ensuring that the cumulative costs are lower than some predetermined cost constraints. We assume that contexts come from a continuous set, that costs can be signed, and that the expected reward and cost functions, while unknown, may be uniformly estimated -- a typical assumption in the literature. In this setting, total cost constraints had so far to be at least of order $T^{3/4}$, where $T$ is the number of rounds, and were even typically assumed to depend linearly on $T$. We are however motivated to use CBwK to impose a fairness constraint of equalized average costs between groups: the budget associated with the corresponding cost constraints should be as close as possible to the natural deviations, of order $\sqrt{T}$. To that end, we introduce a dual strategy based on projected-gradient-descent updates, that is able to deal with total-cost constraints of the order of $\sqrt{T}$ up to poly-logarithmic terms. This strategy is more direct and simpler than existing strategies in the literature. It relies on a careful, adaptive, tuning of the step size.  ( 3 min )

In this work, we theoretically investigate the generalization properties of neural networks (NN) trained by stochastic gradient descent (SGD) algorithm with large learning rates. Under such a training regime, our finding is that, the oscillation of the NN weights caused by the large learning rate SGD training turns out to be beneficial to the generalization of the NN, which potentially improves over the same NN trained by SGD with small learning rates that converges more smoothly. In view of this finding, we call such a phenomenon "benign oscillation". Our theory towards demystifying such a phenomenon builds upon the feature learning perspective of deep learning. Specifically, we consider a feature-noise data generation model that consists of (i) weak features which have a small $\ell_2$-norm and appear in each data point; (ii) strong features which have a larger $\ell_2$-norm but only appear in a certain fraction of all data points; and (iii) noise. We prove that NNs trained by oscillating SGD with a large learning rate can effectively learn the weak features in the presence of those strong features. In contrast, NNs trained by SGD with a small learning rate can only learn the strong features but makes little progress in learning the weak features. Consequently, when it comes to the new testing data which consist of only weak features, the NN trained by oscillating SGD with a large learning rate could still make correct predictions consistently, while the NN trained by small learning rate SGD fails. Our theory sheds light on how large learning rate training benefits the generalization of NNs. Experimental results demonstrate our finding on "benign oscillation".  ( 3 min )

Particle-based deep generative models, such as gradient flows and score-based diffusion models, have recently gained traction thanks to their striking performance. Their principle of displacing particle distributions using differential equations is conventionally seen as opposed to the previously widespread generative adversarial networks (GANs), which involve training a pushforward generator network. In this paper we challenge this interpretation, and propose a novel framework that unifies particle and adversarial generative models by framing generator training as a generalization of particle models. This suggests that a generator is an optional addition to any such generative model. Consequently, integrating a generator into a score-based diffusion model and training a GAN without a generator naturally emerge from our framework. We empirically test the viability of these original models as proofs of concepts of potential applications of our framework.  ( 2 min )

We consider nonstationary multi-armed bandit problems where the model parameters of the arms change over time. We introduce the adaptive resetting bandit (ADR-bandit), a bandit algorithm class that leverages adaptive windowing techniques from literature on data streams. We first provide new guarantees on the quality of estimators resulting from adaptive windowing techniques, which are of independent interest. Furthermore, we conduct a finite-time analysis of ADR-bandit in two typical environments: an abrupt environment where changes occur instantaneously and a gradual environment where changes occur progressively. We demonstrate that ADR-bandit has nearly optimal performance when abrupt or gradual changes occur in a coordinated manner that we call global changes. We demonstrate that forced exploration is unnecessary when we assume such global changes. Unlike the existing nonstationary bandit algorithms, ADR-bandit has optimal performance in stationary environments as well as nonstationary environments with global changes. Our experiments show that the proposed algorithms outperform the existing approaches in synthetic and real-world environments.  ( 2 min )

Low-Rank Adaptation (LoRA), a parameter-efficient fine-tuning method that leverages low-rank adaptation of weight matrices, has emerged as a prevalent technique for fine-tuning pre-trained models such as large language models and diffusion models. Despite its huge success in practice, the theoretical underpinnings of LoRA have largely remained unexplored. This paper takes the first step to bridge this gap by theoretically analyzing the expressive power of LoRA. We prove that, for fully connected neural networks, LoRA can adapt any model $f$ to accurately represent any smaller target model $\overline{f}$ if LoRA-rank $\geq(\text{width of }f) \times \frac{\text{depth of }\overline{f}}{\text{depth of }f}$. We also quantify the approximation error when LoRA-rank is lower than the threshold. For Transformer networks, we show any model can be adapted to a target model of the same size with rank-$(\frac{\text{embedding size}}{2})$ LoRA adapters.  ( 2 min )

Flexible models for probability distributions are an essential ingredient in many machine learning tasks. We develop and investigate a new class of probability distributions, which we call a Squared Neural Family (SNEFY), formed by squaring the 2-norm of a neural network and normalising it with respect to a base measure. Following the reasoning similar to the well established connections between infinitely wide neural networks and Gaussian processes, we show that SNEFYs admit closed form normalising constants in many cases of interest, thereby resulting in flexible yet fully tractable density models. SNEFYs strictly generalise classical exponential families, are closed under conditioning, and have tractable marginal distributions. Their utility is illustrated on a variety of density estimation, conditional density estimation, and density estimation with missing data tasks.  ( 2 min )

We generalize the continuous time framework for score-based generative models from an underlying Brownian motion (BM) to an approximation of fractional Brownian motion (FBM). We derive a continuous reparameterization trick and the reverse time model by representing FBM as a stochastic integral over a family of Ornstein-Uhlenbeck processes to define generative fractional diffusion models (GFDM) with driving noise converging to a non-Markovian process of infinite quadratic variation. The Hurst index $H\in(0,1)$ of FBM enables control of the roughness of the distribution transforming path. To the best of our knowledge, this is the first attempt to build a generative model upon a stochastic process with infinite quadratic variation.  ( 2 min )

Neural additive models (NAMs) can improve the interpretability of deep neural networks by handling input features in separate additive sub-networks. However, they lack inherent mechanisms that provide calibrated uncertainties and enable selection of relevant features and interactions. Approaching NAMs from a Bayesian perspective, we enhance them in three primary ways, namely by a) providing credible intervals for the individual additive sub-networks; b) estimating the marginal likelihood to perform an implicit selection of features via an empirical Bayes procedure; and c) enabling a ranking of feature pairs as candidates for second-order interaction in fine-tuned models. In particular, we develop Laplace-approximated NAMs (LA-NAMs), which show improved empirical performance on tabular datasets and challenging real-world medical tasks.  ( 2 min )

Like many optimizers, Bayesian optimization often falls short of gaining user trust due to opacity. While attempts have been made to develop human-centric optimizers, they typically assume user knowledge is well-specified and error-free, employing users mainly as supervisors of the optimization process. We relax these assumptions and propose a more balanced human-AI partnership with our Collaborative and Explainable Bayesian Optimization (CoExBO) framework. Instead of explicitly requiring a user to provide a knowledge model, CoExBO employs preference learning to seamlessly integrate human insights into the optimization, resulting in algorithmic suggestions that resonate with user preference. CoExBO explains its candidate selection every iteration to foster trust, empowering users with a clearer grasp of the optimization. Furthermore, CoExBO offers a no-harm guarantee, allowing users to make mistakes; even with extreme adversarial interventions, the algorithm converges asymptotically to a vanilla Bayesian optimization. We validate CoExBO's efficacy through human-AI teaming experiments in lithium-ion battery design, highlighting substantial improvements over conventional methods.  ( 2 min )

The evaluation of clustering algorithms can involve running them on a variety of benchmark problems, and comparing their outputs to the reference, ground-truth groupings provided by experts. Unfortunately, many research papers and graduate theses consider only a small number of datasets. Also, the fact that there can be many equally valid ways to cluster a given problem set is rarely taken into account. In order to overcome these limitations, we have developed a framework whose aim is to introduce a consistent methodology for testing clustering algorithms. Furthermore, we have aggregated, polished, and standardised many clustering benchmark dataset collections referred to across the machine learning and data mining literature, and included new datasets of different dimensionalities, sizes, and cluster types. An interactive datasets explorer, the documentation of the Python API, a description of the ways to interact with the framework from other programming languages such as R or MATLAB, and other details are all provided at .  ( 2 min )

Stein thinning is a promising algorithm proposed by (Riabiz et al., 2022) for post-processing outputs of Markov chain Monte Carlo (MCMC). The main principle is to greedily minimize the kernelized Stein discrepancy (KSD), which only requires the gradient of the log-target distribution, and is thus well-suited for Bayesian inference. The main advantages of Stein thinning are the automatic remove of the burn-in period, the correction of the bias introduced by recent MCMC algorithms, and the asymptotic properties of convergence towards the target distribution. Nevertheless, Stein thinning suffers from several empirical pathologies, which may result in poor approximations, as observed in the literature. In this article, we conduct a theoretical analysis of these pathologies, to clearly identify the mechanisms at stake, and suggest improved strategies. Then, we introduce the regularized Stein thinning algorithm to alleviate the identified pathologies. Finally, theoretical guarantees and extensive experiments show the high efficiency of the proposed algorithm. An implementation of regularized Stein thinning as the kernax library in python and JAX is available at https://gitlab.com/drti/kernax.  ( 3 min )

The out-of-sample error (OO) is the main quantity of interest in risk estimation and model selection. Leave-one-out cross validation (LO) offers a (nearly) distribution-free yet computationally demanding approach to estimate OO. Recent theoretical work showed that approximate leave-one-out cross validation (ALO) is a computationally efficient and statistically reliable estimate of LO (and OO) for generalized linear models with differentiable regularizers. For problems involving non-differentiable regularizers, despite significant empirical evidence, the theoretical understanding of ALO's error remains unknown. In this paper, we present a novel theory for a wide class of problems in the generalized linear model family with non-differentiable regularizers. We bound the error |ALO - LO| in terms of intuitive metrics such as the size of leave-i-out perturbations in active sets, sample size n, number of features p and regularization parameters. As a consequence, for the $\ell_1$-regularized problems, we show that |ALO - LO| goes to zero as p goes to infinity while n/p and SNR are fixed and bounded.  ( 2 min )

Many real-world domains require safe decision making in uncertain environments. In this work, we introduce a deep reinforcement learning framework for approaching this important problem. We consider a distribution over transition models, and apply a risk-averse perspective towards model uncertainty through the use of coherent distortion risk measures. We provide robustness guarantees for this framework by showing it is equivalent to a specific class of distributionally robust safe reinforcement learning problems. Unlike existing approaches to robustness in deep reinforcement learning, however, our formulation does not involve minimax optimization. This leads to an efficient, model-free implementation of our approach that only requires standard data collection from a single training environment. In experiments on continuous control tasks with safety constraints, we demonstrate that our framework produces robust performance and safety at deployment time across a range of perturbed test environments.  ( 2 min )

A common goal in network modeling is to uncover the latent community structure present among nodes. For many real-world networks, the true connections consist of events arriving as streams, which are then aggregated to form edges, ignoring the dynamic temporal component. A natural way to take account of these temporal dynamics of interactions is to use point processes as the foundation of network models for community detection. Computational complexity hampers the scalability of such approaches to large sparse networks. To circumvent this challenge, we propose a fast online variational inference algorithm for estimating the latent structure underlying dynamic event arrivals on a network, using continuous-time point process latent network models. We describe this procedure for networks models capturing community structure. This structure can be learned as new events are observed on the network, updating the inferred community assignments. We investigate the theoretical properties of such an inference scheme, and provide regret bounds on the loss function of this procedure. The proposed inference procedure is then thoroughly compared, using both simulation studies and real data, to non-online variants. We demonstrate that online inference can obtain comparable performance, in terms of community recovery, to non-online variants, while realising computational gains. Our proposed inference framework can also be readily modified to incorporate other popular network structures.  ( 3 min )

Machine learning tools often rely on embedding text as vectors of real numbers. In this paper, we study how the semantic structure of language is encoded in the algebraic structure of such embeddings. Specifically, we look at a notion of ``semantic independence'' capturing the idea that, e.g., ``eggplant'' and ``tomato'' are independent given ``vegetable''. Although such examples are intuitive, it is difficult to formalize such a notion of semantic independence. The key observation here is that any sensible formalization should obey a set of so-called independence axioms, and thus any algebraic encoding of this structure should also obey these axioms. This leads us naturally to use partial orthogonality as the relevant algebraic structure. We develop theory and methods that allow us to demonstrate that partial orthogonality does indeed capture semantic independence. Complementary to this, we also introduce the concept of independence preserving embeddings where embeddings preserve the conditional independence structures of a distribution, and we prove the existence of such embeddings and approximations to them.  ( 2 min )

Gaussian process (GP) hyperparameter optimization requires repeatedly solving linear systems with $n \times n$ kernel matrices. To address the prohibitive $\mathcal{O}(n^3)$ time complexity, recent work has employed fast iterative numerical methods, like conjugate gradients (CG). However, as datasets increase in magnitude, the corresponding kernel matrices become increasingly ill-conditioned and still require $\mathcal{O}(n^2)$ space without partitioning. Thus, while CG increases the size of datasets GPs can be trained on, modern datasets reach scales beyond its applicability. In this work, we propose an iterative method which only accesses subblocks of the kernel matrix, effectively enabling \emph{mini-batching}. Our algorithm, based on alternating projection, has $\mathcal{O}(n)$ per-iteration time and space complexity, solving many of the practical challenges of scaling GPs to very large datasets. Theoretically, we prove our method enjoys linear convergence and empirically we demonstrate its robustness to ill-conditioning. On large-scale benchmark datasets up to four million datapoints our approach accelerates training by a factor of 2$\times$ to 27$\times$ compared to CG.  ( 2 min )

In this paper we address the problem of learning and backtesting inventory control policies in the presence of general arrival dynamics -- which we term as a quantity-over-time arrivals model (QOT). We also allow for order quantities to be modified as a post-processing step to meet vendor constraints such as order minimum and batch size constraints -- a common practice in real supply chains. To the best of our knowledge this is the first work to handle either arbitrary arrival dynamics or an arbitrary downstream post-processing of order quantities. Building upon recent work (Madeka et al., 2022) we similarly formulate the periodic review inventory control problem as an exogenous decision process, where most of the state is outside the control of the agent. Madeka et al. (2022) show how to construct a simulator that replays historic data to solve this class of problem. In our case, we incorporate a deep generative model for the arrivals process as part of the history replay. By formulating the problem as an exogenous decision process, we can apply results from Madeka et al. (2022) to obtain a reduction to supervised learning. Finally, we show via simulation studies that this approach yields statistically significant improvements in profitability over production baselines. Using data from an ongoing real-world A/B test, we show that Gen-QOT generalizes well to off-policy data.  ( 3 min )

Large learning rates, when applied to gradient descent for nonconvex optimization, yield various implicit biases including the edge of stability (Cohen et al., 2021), balancing (Wang et al., 2022), and catapult (Lewkowycz et al., 2020). These phenomena cannot be well explained by classical optimization theory. Though significant theoretical progress has been made in understanding these implicit biases, it remains unclear for which objective functions would they occur. This paper provides an initial step in answering this question, namely that these implicit biases are in fact various tips of the same iceberg. They occur when the objective function of optimization has some good regularity, which, in combination with a provable preference of large learning rate gradient descent for moving toward flatter regions, results in these nontrivial dynamical phenomena. To establish this result, we develop a new global convergence theory under large learning rates, for a family of nonconvex functions without globally Lipschitz continuous gradient, which was typically assumed in existing convergence analysis. A byproduct is the first non-asymptotic convergence rate bound for large-learning-rate gradient descent optimization of nonconvex functions. We also validate our theory with experiments on neural networks, where different losses, activation functions, and batch normalization all can significantly affect regularity and lead to very different training dynamics.  ( 3 min )

We design and analyze reinforcement learning algorithms for Graphon Mean-Field Games (GMFGs). In contrast to previous works that require the precise values of the graphons, we aim to learn the Nash Equilibrium (NE) of the regularized GMFGs when the graphons are unknown. Our contributions are threefold. First, we propose the Proximal Policy Optimization for GMFG (GMFG-PPO) algorithm and show that it converges at a rate of $O(T^{-1/3})$ after $T$ iterations with an estimation oracle, improving on a previous work by Xie et al. (ICML, 2021). Second, using kernel embedding of distributions, we design efficient algorithms to estimate the transition kernels, reward functions, and graphons from sampled agents. Convergence rates are then derived when the positions of the agents are either known or unknown. Results for the combination of the optimization algorithm GMFG-PPO and the estimation algorithm are then provided. These algorithms are the first specifically designed for learning graphons from sampled agents. Finally, the efficacy of the proposed algorithms are corroborated through simulations. These simulations demonstrate that learning the unknown graphons reduces the exploitability effectively.  ( 2 min )

In this paper, we critically examine the prevalent practice of using additive mixtures of Mat\'ern kernels in single-output Gaussian process (GP) models and explore the properties of multiplicative mixtures of Mat\'ern kernels for multi-output GP models. For the single-output case, we derive a series of theoretical results showing that the smoothness of a mixture of Mat\'ern kernels is determined by the least smooth component and that a GP with such a kernel is effectively equivalent to the least smooth kernel component. Furthermore, we demonstrate that none of the mixing weights or parameters within individual kernel components are identifiable. We then turn our attention to multi-output GP models and analyze the identifiability of the covariance matrix $A$ in the multiplicative kernel $K(x,y) = AK_0(x,y)$, where $K_0$ is a standard single output kernel such as Mat\'ern. We show that $A$ is identifiable up to a multiplicative constant, suggesting that multiplicative mixtures are well suited for multi-output tasks. Our findings are supported by extensive simulations and real applications for both single- and multi-output settings. This work provides insight into kernel selection and interpretation for GP models, emphasizing the importance of choosing appropriate kernel structures for different tasks.  ( 2 min )

We study the bias of Stochastic Gradient Descent (SGD) to learn low-rank weight matrices when training deep neural networks. Our results show that training neural networks with mini-batch SGD and weight decay causes a bias towards rank minimization over the weight matrices. Specifically, we show, both theoretically and empirically, that this bias is more pronounced when using smaller batch sizes, higher learning rates, or increased weight decay. Additionally, we predict and observe empirically that weight decay is necessary to achieve this bias. Unlike previous literature, our analysis does not rely on assumptions about the data, convergence, or optimality of the weight matrices and applies to a wide range of neural network architectures of any width or depth. Finally, we empirically investigate the connection between this bias and generalization, finding that it has a marginal effect on generalization.  ( 2 min )

A Bayesian coreset is a small, weighted subset of data that replaces the full dataset during inference in order to reduce computational cost. However, state of the art methods for tuning coreset weights are expensive, require nontrivial user input, and impose constraints on the model. In this work, we propose a new method -- Coreset MCMC -- that simulates a Markov chain targeting the coreset posterior, while simultaneously updating the coreset weights using those same draws. Coreset MCMC is simple to implement and tune, and can be used with any existing MCMC kernel. We analyze Coreset MCMC in a representative setting to obtain key insights about the convergence behaviour of the method. Empirical results demonstrate that Coreset MCMC provides higher quality posterior approximations and reduced computational cost compared with other coreset construction methods. Further, compared with other general subsampling MCMC methods, we find that Coreset MCMC has a higher sampling efficiency with competitively accurate posterior approximations.  ( 2 min )

Feedforward neural network (FFN) and two specific types of recurrent neural network, long short-term memory (LSTM) and gated recurrent unit (GRU), are used for modeling US recessions in the period from 1967 to 2021. The estimated models are then employed to conduct real-time predictions of the Great Recession and the Covid-19 recession in US. Their predictive performances are compared to those of the traditional linear models, the logistic regression model both with and without the ridge penalty. The out-of-sample performance suggests the application of LSTM and GRU in the area of recession forecasting, especially for the long-term forecasting tasks. They outperform other types of models across 5 forecasting horizons with respect to different types of statistical performance metrics. Shapley additive explanations (SHAP) method is applied to the fitted GRUs across different forecasting horizons to gain insight into the feature importance. The evaluation of predictor importance differs between the GRU and ridge logistic regression models, as reflected in the variable order determined by SHAP values. When considering the top 5 predictors, key indicators such as the S\&P 500 index, real GDP, and private residential fixed investment consistently appear for short-term forecasts (up to 3 months). In contrast, for longer-term predictions (6 months or more), the term spread and producer price index become more prominent. These findings are supported by both local interpretable model-agnostic explanations (LIME) and marginal effects.  ( 3 min )

We consider gradient flow/gradient descent and heavy ball/accelerated gradient descent optimization for convex objective functions. In the gradient flow case, we prove the following: 1. If $f$ does not have a minimizer, the convergence $f(x_t)\to \inf f$ can be arbitrarily slow. 2. If $f$ does have a minimizer, the excess energy $f(x_t) - \inf f$ is integrable/summable in time. In particular, $f(x_t) - \inf f = o(1/t)$ as $t\to\infty$. 3. In Hilbert spaces, this is optimal: $f(x_t) - \inf f$ can decay to $0$ as slowly as any given function which is monotone decreasing and integrable at $\infty$, even for a fixed quadratic objective. 4. In finite dimension (or more generally, for all gradient flow curves of finite length), this is not optimal: We prove that there are convex monotone decreasing integrable functions $g(t)$ which decrease to zero slower than $f(x_t)-\inf f$ for the gradient flow of any convex function on $\mathbb R^d$. For instance, we show that any gradient flow $x_t$ of a convex function $f$ in finite dimension satisfies $\liminf_{t\to\infty} \big(t\cdot \log^2(t)\cdot \big\{f(x_t) -\inf f\big\}\big)=0$. This improves on the commonly reported $O(1/t)$ rate and provides a sharp characterization of the energy decay law. We also note that it is impossible to establish a rate $O(1/(t\phi(t))$ for any function $\phi$ which satisfies $\lim_{t\to\infty}\phi(t) = \infty$, even asymptotically. Similar results are obtained in related settings for (1) discrete time gradient descent, (2) stochastic gradient descent with multiplicative noise and (3) the heavy ball ODE. In the case of stochastic gradient descent, the summability of $\mathbb E[f(x_n) - \inf f]$ is used to prove that $f(x_n)\to \inf f$ almost surely - an improvement on the convergence almost surely up to a subsequence which follows from the $O(1/n)$ decay estimate.  ( 3 min )

Accurate estimation of conditional average treatment effects (CATE) is at the core of personalized decision making. While there is a plethora of models for CATE estimation, model selection is a nontrivial task, due to the fundamental problem of causal inference. Recent empirical work provides evidence in favor of proxy loss metrics with double robust properties and in favor of model ensembling. However, theoretical understanding is lacking. Direct application of prior theoretical work leads to suboptimal oracle model selection rates due to the non-convexity of the model selection problem. We provide regret rates for the major existing CATE ensembling approaches and propose a new CATE model ensembling approach based on Q-aggregation using the doubly robust loss. Our main result shows that causal Q-aggregation achieves statistically optimal oracle model selection regret rates of $\frac{\log(M)}{n}$ (with $M$ models and $n$ samples), with the addition of higher-order estimation error terms related to products of errors in the nuisance functions. Crucially, our regret rate does not require that any of the candidate CATE models be close to the truth. We validate our new method on many semi-synthetic datasets and also provide extensions of our work to CATE model selection with instrumental variables and unobserved confounding.  ( 2 min )

In some settings neural networks exhibit a phenomenon known as grokking, where they achieve perfect or near-perfect accuracy on the validation set long after the same performance has been achieved on the training set. In this paper, we discover that grokking is not limited to neural networks but occurs in other settings such as Gaussian process (GP) classification, GP regression and linear regression. We also uncover a mechanism by which to induce grokking on algorithmic datasets via the addition of dimensions containing spurious information. The presence of the phenomenon in non-neural architectures provides evidence that grokking is not specific to SGD or weight norm regularisation. Instead, grokking may be possible in any setting where solution search is guided by complexity and error. Based on this insight and further trends we see in the training trajectories of a Bayesian neural network (BNN) and GP regression model, we make progress towards a more general theory of grokking. Specifically, we hypothesise that the phenomenon is governed by the accessibility of certain regions in the error and complexity landscapes.  ( 2 min )

The decision tree is a flexible machine learning model that finds its success in numerous applications. It is usually fitted in a recursively greedy manner using CART. In this paper, we investigate the convergence rate of CART under a regression setting. First, we establish an upper bound on the prediction error of CART under a sufficient impurity decrease (SID) condition \cite{chi2022asymptotic} -- our result improves upon the known result by \cite{chi2022asymptotic} under a similar assumption. Furthermore, we provide examples that demonstrate the error bound cannot be further improved by more than a constant or a logarithmic factor. Second, we introduce a set of easily verifiable sufficient conditions for the SID condition. Specifically, we demonstrate that the SID condition can be satisfied in the case of an additive model, provided that the component functions adhere to a ``locally reverse Poincar{\'e} inequality". We discuss several well-known function classes in non-parametric estimation to illustrate the practical utility of this concept.  ( 2 min )

In many scenarios, one uses a large training set to train a model with the goal of performing well on a smaller testing set with a different distribution. Learning a weight for each data point of the training set is an appealing solution, as it ideally allows one to automatically learn the importance of each training point for generalization on the testing set. This task is usually formalized as a bilevel optimization problem. Classical bilevel solvers are based on a warm-start strategy where both the parameters of the models and the data weights are learned at the same time. We show that this joint dynamic may lead to sub-optimal solutions, for which the final data weights are very sparse. This finding illustrates the difficulty of data reweighting and offers a clue as to why this method is rarely used in practice.  ( 2 min )

Biases with respect to socially-salient attributes of individuals have been well documented in evaluation processes used in settings such as admissions and hiring. We view such an evaluation process as a transformation of a distribution of the true utility of an individual for a task to an observed distribution and model it as a solution to a loss minimization problem subject to an information constraint. Our model has two parameters that have been identified as factors leading to biases: the resource-information trade-off parameter in the information constraint and the risk-averseness parameter in the loss function. We characterize the distributions that arise from our model and study the effect of the parameters on the observed distribution. The outputs of our model enrich the class of distributions that can be used to capture variation across groups in the observed evaluations. We empirically validate our model by fitting real-world datasets and use it to study the effect of interventions in a downstream selection task. These results contribute to an understanding of the emergence of bias in evaluation processes and provide tools to guide the deployment of interventions to mitigate biases.  ( 2 min )

Incorporating expert demonstrations has empirically helped to improve the sample efficiency of reinforcement learning (RL). This paper quantifies theoretically to what extent this extra information reduces RL's sample complexity. In particular, we study the demonstration-regularized reinforcement learning that leverages the expert demonstrations by KL-regularization for a policy learned by behavior cloning. Our findings reveal that using $N^{\mathrm{E}}$ expert demonstrations enables the identification of an optimal policy at a sample complexity of order $\widetilde{\mathcal{O}}(\mathrm{Poly}(S,A,H)/(\varepsilon^2 N^{\mathrm{E}}))$ in finite and $\widetilde{\mathcal{O}}(\mathrm{Poly}(d,H)/(\varepsilon^2 N^{\mathrm{E}}))$ in linear Markov decision processes, where $\varepsilon$ is the target precision, $H$ the horizon, $A$ the number of action, $S$ the number of states in the finite case and $d$ the dimension of the feature space in the linear case. As a by-product, we provide tight convergence guarantees for the behaviour cloning procedure under general assumptions on the policy classes. Additionally, we establish that demonstration-regularized methods are provably efficient for reinforcement learning from human feedback (RLHF). In this respect, we provide theoretical evidence showing the benefits of KL-regularization for RLHF in tabular and linear MDPs. Interestingly, we avoid pessimism injection by employing computationally feasible regularization to handle reward estimation uncertainty, thus setting our approach apart from the prior works.  ( 2 min )

We present two neural network approaches that approximate the solutions of static and dynamic conditional optimal transport (COT) problems, respectively. Both approaches enable sampling and density estimation of conditional probability distributions, which are core tasks in Bayesian inference. Our methods represent the target conditional distributions as transformations of a tractable reference distribution and, therefore, fall into the framework of measure transport. COT maps are a canonical choice within this framework, with desirable properties such as uniqueness and monotonicity. However, the associated COT problems are computationally challenging, even in moderate dimensions. To improve the scalability, our numerical algorithms leverage neural networks to parameterize COT maps. Our methods exploit the structure of the static and dynamic formulations of the COT problem. PCP-Map models conditional transport maps as the gradient of a partially input convex neural network (PICNN) and uses a novel numerical implementation to increase computational efficiency compared to state-of-the-art alternatives. COT-Flow models conditional transports via the flow of a regularized neural ODE; it is slower to train but offers faster sampling. We demonstrate their effectiveness and efficiency by comparing them with state-of-the-art approaches using benchmark datasets and Bayesian inverse problems.  ( 2 min )

Hello all, Does anyone know any company (paid or free) that would allow me to generate specific content based on current events? Ideally something that I can integrate in my own site. ​ cheers submitted by /u/JYanezez [link] [comments]  ( 9 min )

Urban planning is tricky - governments push top-down changes while locals want bottom-up ideas. It's hard to find compromises that make everyone happier. A new research paper proposes using Multi-Agent Reinforcement Learning (MARL) to vote on land use. Some agents represent officials, others are for residents. The AI is trained to balance competing interests. It learns to optimize for "consensus rewards" that keep all sides content. The AI acted like an impartial mediator to find win-win solutions. Testing on a real neighborhood showed the AI model: Created more sustainable land use per city goals Improved the variety of housing/shops to liven up the area Made the end results more fair for lower/middle/upper income folks There's more details on how the model was evaluated in the paper. There were a number of different metrics used to score the model's results. I like how they turned urban planning into a spatial graph that the AI can process. This seems like a pretty interesting approach - although there are some limits like relying on a lot of land parcel data that seems hard to find for larger communities. TLDR: AI helps find compromises in urban planning that balance government and community interests more fairly. Full summary is here. Paper is here. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

submitted by /u/thisisinsider [link] [comments]  ( 8 min )

News provided by aibrews.com ​ Twelve Labs announced video-language foundation model Pegasus-1 (80B) along with a new suite of Video-to-Text APIs. Pegasus-1 integrates visual, audio, and speech information to generate more holistic text from videos, achieving the new state-of-the-art performance in video summarization benchmarks [Details]. Segmind announced open-source SSD-1B, the fastest diffusion-based text-to-image model. SSD-1B is 50% smaller and 60% faster compared to the SDXL 1.0 model with a minimal impact on image quality when compared to SDXL 1.0. Segmind has licensed it for commercial use [Detail]. BostonDynamics has created a robot tour guide using Spot integrated with Chat GPT and other AI models as a proof of concept for the robotics applications of foundational models […  ( 10 min )

I added 4 photos of Elijah Wood on Remini and 4 photos of Tobey Maguire, this is how Elijah Maguire was born. submitted by /u/Skystalker815 [link] [comments]  ( 8 min )

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Engaging with a more intelligent AI can act as a cognitive catalyst for human intelligence in several ways. First, the AI can rapidly introduce new concepts and frameworks that you might not have encountered, effectively accelerating your learning curve. It serves as an optimized information filter, presenting only what's most relevant and impactful for cognitive development. Second, talking to a smarter AI can refine your critical thinking skills. When you're posed with challenging questions or offered complex solutions, you're compelled to dissect the information logically. This constant mental exercise can sharpen your analytical abilities over time. Third, the AI's ability to recall and connect disparate pieces of information can encourage you to look for patterns and links in your own thinking. This interconnected way of understanding the world can improve your problem-solving skills, as you start to recognize that many issues are multifaceted and interconnected. Fourth, unlike a human counterpart who might be swayed by emotional reasoning or biases, a more intelligent AI operates on rational algorithms. Interacting with such a model pushes you to formulate your arguments more rigorously, thereby honing your logical reasoning skills. Fifth, by observing the AI's methods of discourse and argumentation, you can learn more effective communication skills. This is particularly useful for conveying complex ideas in a coherent, easy-to-understand manner, a key trait of intelligence. Overall, the cumulative effect of these interactions can significantly boost your own intellectual capabilities. It's not just about absorbing new information; it's about upgrading the way you process and apply that information, thereby elevating your overall cognitive function. CGPT-4 submitted by /u/Georgeo57 [link] [comments]  ( 9 min )

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TL;DR: Our new paper presents SCALE, a technique that improves hallucination detection capabilities of pre-trained models over short and long documents with no fine-tuning necessary by evaluating hypotheses against large chunks of text as opposed to the traditional sentence-by-sentence approach. Furthermore, we introduce ScreenEval — the most extensive dialogue-based dataset for factual inconsistency detection on long documents to date. https://preview.redd.it/ihqok954ntwb1.png?width=1964&format=png&auto=webp&s=70dbce6f913b2909b7d170959077c1fe19791c42 Title: Fast and Accurate Factual Inconsistency Detection Over Long Documents Installation: pip install scale-score Paper: https://arxiv.org/abs/2310.13189 Code: https://github.com/asappresearch/scale-score Abstract: Generative AI models…  ( 9 min )

Say you are a potato chips company. The goal is to have consumers upload images of the product they are having issues with and be able to identify the product by brand/variant using machine learning. Consumers can upload real product photos that they have taken, or upload bogus images from the internet, or even upload completely irrelevant/inappropriate photos (like that of a dog or cat). ​ real image ​ web image 1 ​ web image 2 ​ bogus image In this example, for the legitimate image, the goal is to classify it as "Lays Classic". There might be products that are not in bag form, such as those in tubes. Furthermore, the images taken can be in different lighting conditions/orientations. Some images might have other products as well. I have been out of the ML field for the past 4 years so I'm not up to date on the most state of the art methods for this problem. I have studied CNNs 4 years ago, but there has been advances like transformer based methods. Someone has tried ResNet-50 and YOLOv5, and I'm thinking about using a pretrained model like CLIP and just train the final classification layer. But I would appreciate to hear from someone more well versed what recommended approach to take as far as model/labeling/number of images needed per class, etc. It might be that I would need multiple models, such as one to identify the legitimate images from the rest, and then another one to identify the product/variants. Any advice would be welcome. Thanks submitted by /u/EyeTechnical7643 [link] [comments]  ( 9 min )

PAPER: https://arxiv.org/abs/2310.16764 SUMMARY The paper "ConvNets Match Vision Transformers at Scale" from Google DeepMind aims to debunk the prevalent notion that Vision Transformers (ViTs) are inherently superior to ConvNets for large-scale image classification. Using the NFNet model family as a representative ConvNet architecture, the authors pre-train various models on the extensive JFT-4B dataset under different compute budgets, ranging from 0.4k to 110k TPU-v4 core hours. Through this empirical analysis, they observe a log-log scaling law between held-out loss and compute budget. Importantly, when these NFNets are fine-tuned on ImageNet, they match the performance metrics of ViTs trained under comparable computational constraints. Their most resource-intensive model even achieves a Top-1 ImageNet accuracy of 90.4%. The crux of the paper's argument is that the supposed performance gap between ConvNets and ViTs largely vanishes under a fair comparison, which accounts for compute and data scale. In other words, the efficacy of a machine learning model in large-scale image classification is more dependent on the available data and computational resources than on the choice between ConvNet and Vision Transformer architectures. This challenges the community's leaning towards ViTs and emphasizes the importance of equitable benchmarking when evaluating different neural network architectures. submitted by /u/psyyduck [link] [comments]  ( 9 min )

Hello, I'm making my first post here, and I'm hoping to tap into the collective wisdom of this platform. I have a background in Machine Learning (ML) with a good grasp of the underlying mathematics, and I've taken several related courses during my grad school days. After a hiatus, I'm diving back into ML and exploring the latest in the field. I recently went through the "Attention is All You Need" paper, along with some related literature, and I’m eager to put my knowledge into practice. However, I find myself at a crossroads and would appreciate some guidance. I've been exploring Hugging Face for implementing ML models, but I'm not completely sold on using it for production. The design and documentation have proven to be a bit challenging to navigate, and I find myself wondering if I mi…  ( 10 min )

Please elaborate on this. Including your role at the company, your day to day tasks, tools and languages you’re using. Thank you in advance! submitted by /u/Judessaa [link] [comments]  ( 9 min )

I have to write a master's thesis (60-70 pages) for my AI Msc. I have soon learned about sparse state space models and find them interesting. I am also interested in stock price prediction. Now I am reading articles but find it hard to propose some novel idea. How do I do it? Do any unsolved problems in that field exist? submitted by /u/Pineapple_throw_105 [link] [comments]  ( 9 min )

Been working on my platform for a while now and one of the key features is the data request feature, which allows users to submit data requests for free. These requests include descriptions, required fields, geographical scope, budget etc... The data requests get sent to tons of companies, organization, people and they will reach out to you directly. No matter the dataset (as long as it's legal of course). If you need to train a model on the dataset, I'm also integrating a plug-and-play ML training infrastructure that can generate scripts for you depending on your need. If that's something that interests you, feel free to reach out! Note: You can also upload open-source datasets on the platform if you're feeling like it! We have a donation link for data contributors :) submitted by /u/nobilis_rex_ [link] [comments]  ( 9 min )

Urban planning is tricky - governments push top-down changes while locals want bottom-up ideas. It's hard to find compromises that make everyone happier. A new research paper proposes using Multi-Agent Reinforcement Learning (MARL) to vote on land use. Some agents represent officials, others are for residents. The AI is trained to balance competing interests. It learns to optimize for "consensus rewards" that keep all sides content. The AI acted like an impartial mediator to find win-win solutions. Testing on a real neighborhood showed the AI model: Created more sustainable land use per city goals Improved the variety of housing/shops to liven up the area Made the end results more fair for lower/middle/upper income folks There's more details on how the model was evaluated in the paper. There were a number of different metrics used to score the model's results. I like how they turned urban planning into a spatial graph that the AI can process. This seems like a pretty interesting approach - although there are some limits like relying on a lot of land parcel data that seems hard to find for larger communities. TLDR: AI helps find compromises in urban planning that balance government and community interests more fairly. Full summary is here. Paper is here. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

What are the benefits of using an H100 over an A100 (both at 80 GB and both using FP16) for LLM inference? ​ Seeing the datasheet for both GPUS, the H100 has twice the max flops, but they have almost the same memory bandwidth (2000 GB/sec). As memory latency dominates inference, I wonder what benefits the H100 has. One benefit could, of course, be the ability to use FP8 (which is extremely useful), but I'm interested in the difference in the hardware specs in this question. submitted by /u/faschu [link] [comments]  ( 9 min )

I am trying to build a summarizer for a conversation that happened between a rule-based bot and a customer. To my disadvantage the working language is Turkish. I gathered fine-tuning data of 1.000 examples. Also, I have a Turkish summarization dataset of +100k. As far as I observed instruction fine-tuning will yield proper results if and only if there is a good amount of examples in the pre-training data of the LLM. Have you had similar experiences with low-resource languages? Any advice on how to tackle such issues? Also, do you know any open-source LLM with a high amount of low-resource language in its pre-training data? submitted by /u/dafajon [link] [comments]  ( 9 min )

Do you know any packages for unsupervised cluster with images (between multiple) without relying on a pre-trained network? Python or R preferred, thanks. Well, download app works too then. submitted by /u/sladebrigade [link] [comments]  ( 9 min )

Paper: https://arxiv.org/abs/2310.16828 Website: https://nicklashansen.github.io/td-mpc2 Code: https://github.com/nicklashansen/tdmpc2 Abstract: TD-MPC is a model-based reinforcement learning (MBRL) algorithm that performs local trajectory optimization in the latent space of a learned implicit (decoder-free) world model. In this work, we present TD-MPC2: a series of improvements upon the TD-MPC algorithm. We demonstrate that TD-MPC2 improves significantly over baselines across 104 online RL tasks spanning 4 diverse task domains, achieving consistently strong results without any hyperparameter tuning. We further show that agent capabilities increase with model and data size, and successfully train a single agent to perform 80 tasks across multiple task domains, embodiments, and action spaces. We conclude with an account of lessons, opportunities, and risks associated with large TD-MPC2 agents. https://preview.redd.it/avdb19jytrwb1.png?width=2678&format=png&auto=webp&s=75d63a044abf304c2d16e318c11e95e8397a3964 submitted by /u/joepadde [link] [comments]  ( 9 min )

My boss is a semi famous author in a niche academic field. I have thousands of pages of text coming from books, transcripts, and more. Is there a straightforward path to creating a corpus to augment Bert or Llama or another llm? End goal being able to chat with this ai that is now trained on his life's work. Is there anything specific to understand in terms of preparing the corpus? Do I need key value pairs where I write a ton of examples questions and responses? submitted by /u/spacedragon13 [link] [comments]  ( 9 min )

Hey y’all Hope I’m in the right spot but I’d like some career advice. I just recent got hired into a small shop with a bunch of CNC machines. And I saw that they made a part for a big aerospace company. Needless to say that got my attention. I don’t want to risk the guy working there because he’s likely not allowed to talk about it. But what language would you recommend to start with to be able to program and machine a part that crazy? Is it an actual computer program such as Java or should I lean into CAD/autoCAD etc? Many thanks in advance submitted by /u/I-farm-celery [link] [comments]  ( 9 min )

We've just released an update to AglieRL, our SOTA evolutionary hyperparameter optimisation framework for reinforcement learning. This update includes a MakeEvolvable wrapper to make any PyTorch network, including pre-trained models, evolvable in one line of code. This can result in 10x faster training by using our framework. We've also created a curriculum learning and self-play tutorial that shows how to train a DQN agent to play Connect Four. Self-play can lead to amazing results, as demonstrated by AlphaGo etc, where agents discover new strategies to achieve superhuman performance, so we wanted to make it accessible to all. Please check it out! https://github.com/AgileRL/AgileRL submitted by /u/nicku_a [link] [comments]  ( 9 min )

Interested in anyone who knows about this company (they have a lot of ML hiring listings right now). Basically wondering if it's worth exploring more. They have apparently raised $240mln and are a unicorn so this is an important topic. Here is the summary of some red flags I have found: 1) Founders have no ML background 2) Zero released product after several years despite huge funding. 3) TC article says founded 2021, but every listing claims it is YC2017. One of the founders did a recruiting service from YC 2017, but Imbue is a totally unrelated company with a different founding group so claiming YC affiliation seems dubious/unethical. YC is also not named as an investor in the company anywhere on their website. 4) No-one I have spoken to has ever worked with them/ heard of them ou…  ( 10 min )

https://arxiv.org/abs/2310.02239 From the paper: Therefore, we introduce a set of special tokens Vimg = {[IMG1], [IMG2], . . . , [IMGn]} (default n = 8) as generative vokens into the LLM’s vocabulary V . The LLM’s output hidden state for these vokens is harnessed for subsequent image generation, and the positions of these vokens can represent the insertion of the interleaved images. What function exactly are these different image tokens serving here? It seems like you should only need one since we are passing on the hidden state anyway? submitted by /u/30299578815310 [link] [comments]  ( 9 min )

Database question /answer with link We have a community app with groups and would like to build a search function where users can ask: when is the next event, give me the messages posted by Erik, where can I find … The information is stored in a table: post-test, posted-by etc. We do not want to use OpenAI or any external apis. Is something like llama index too powerful or are there other solutions for this? And we want to receive the postId to direct the use to the post submitted by /u/dirk_klement [link] [comments]  ( 9 min )

DeepMind released the Training Compute-Optimal Large Language Models paper in 2022 which describe some scaling laws for LLMs. As far as I understand this is the most accredited reference to estimate the optimal relation between dataset size, compute power and model size. Recently a number of models have been developed using far less data, parameters and compute than the bigger LLMs. Yet these models achieved great results thanks to much better data quality. For instance models like WizardLM, TinyStories and phi-1. Similarly, a lot of research seems to imply that better data could offer huge improvements without any other changes. I'm curious about what role the data quality plays in the training of LLMs. Is the set of values estimated by the Chinchilla scaling laws optimal for these smaller models with optimized data too? Do we have any model to estimate the quality of some datasets and some scaling laws that take it into account? Are there any relevant projects or research I could check out, focused on creating big datasets to train larger LLMs with high-quality data? submitted by /u/IAmBlueNebula [link] [comments]  ( 9 min )

Hi, I graduated from a Statistics & ML masters this summer, and have since August started working where I have been assigned a counselor. One big part of this is to help dictate my path forward in accordance with what I want to learn more about. I feel that I have a (in contrast to the people around me) generally good understanding of ML and modeling, but less so about how it is used in a business case. As such, an idea was to attend a machine learning conference which is focused on business implementations to learn more about this, but also get an opportunity to expand my network a bit. Does anyone have a suggestion of such a conference? Preferably in Europe as that would be easier to argue in terms of expenses! :) submitted by /u/Accomplished_Sea1675 [link] [comments]  ( 9 min )

Interview: https://www.youtube.com/watch?v=4aOLxPdx1Dg Abstract: Context window management has become a critical part of every LLM application — from the basics (embeddings models, vector DBs) to more advanced techniques (query rewriting, HyDE, summarization). MemGPT is a new tool from UC Berkeley built by Charles Packer that automates "memory" management for LLMs and creates a functionally infinite context window. Charles joins us this week to talk about MemGPT, the techniques behind it, and where the conversational AI space is headed. submitted by /u/cgwuaqueduct [link] [comments]  ( 9 min )

Hi there - can someone point me in the direction of projects that are incentivizing dataset contributors? I have a background in blockchain and crypto-assets, so I am new to the ML space, but it seems like it's a place where there would be ample crossover...and I haven't found many projects dealing with this. Thanks! submitted by /u/gigstudies [link] [comments]  ( 9 min )

I'm trying to train Resnet50 on Imagenet following this paper [1] as well as this one [2]. ​ They say that at approximately every 30 epochs, I should drop the learning rate by 10. Since I'm training on 8 GPUs, I adjusted the learning rate according to [1]. ​ Original lr= 0.1 Original Batch = 256 Per-GPU lr = 0.025 Per-GPU Batch = 64 ​ The problem I have is that when I divide the learning rate by 10 at convergence (approx 30 epochs), I don't get as much improvement as [1] and [2]. https://preview.redd.it/5ar2g15h1nwb1.png?width=683&format=png&auto=webp&s=3e2751443cea654d9c6366dc4dc9859f0ec7952b Has anyone else had this issue? Any advice? Thanks ​ submitted by /u/mrLiamFa [link] [comments]  ( 9 min )

Posted by Xiaoran "Van" Fan, Experimental Scientist, and Trausti Thormundsson, Director, Google The market for true wireless stereo (TWS) active noise canceling (ANC) hearables (headphones and earbuds) has been soaring in recent years, and the global shipment volume will nearly double that of smart wristbands and watches in 2023. The on-head time for hearables has extended significantly due to the recent advances in ANC, transparency mode, and artificial intelligence. Users frequently wear hearables not just for music listening, but also for exercising, focusing, or simply mood adjustment. However, hearable health is still mostly uncharted territory for the consumer market. In “APG: Audioplethysmography for Cardiac Monitoring in Hearables,” presented at MobiCom 2023, we introduce …  ( 93 min )

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I can see how the epsilon-greedy policy is a valid way to handle the exploration/exploitation tradeoff, but it’s also clearly not the only way, and results in a policy that is discontinuous with respect to the action-values (which intuitively I’d expect to be bad, but I also know a lot of RL/DRL can defy intuition). You could certainly create a policy using the softmax of the action-values, adjusting temperature as desired, for example, among countless other methods of converting logits into a probability distribution. So what makes epsilon-greedy stand out? submitted by /u/KalebMW99 [link] [comments]  ( 9 min )

We've just released an update to AglieRL, our SOTA evolutionary hyperparameter optimisation framework for reinforcement learning. This update includes a MakeEvolvable wrapper to make any PyTorch network, including pre-trained models, evolvable in one line of code. This can result in 10x faster training by using our framework. We've also created a curriculum learning and self-play tutorial that shows how to train a DQN agent to play Connect Four. Self-play can lead to amazing results, as demonstrated by AlphaGo etc, where agents discover new strategies to achieve superhuman performance, so we wanted to make it accessible to all. Please check it out! https://github.com/AgileRL/AgileRL submitted by /u/nicku_a [link] [comments]  ( 9 min )

Hi all, I am following the chapter 4 from the book of Barto & Sutton on RL and I implemented the simple algorithm for value iteration for a given Transition probability matrix. As I kept increasing the state space, this became slower and slower. It seems to me that it is an embarrassingly parallelizable algorithm, since we can compute the value of each state independently (just with the values from the previous iteration). Is there any example online on how to do it efficiently with pytorch or any other library? ​ submitted by /u/nlp7s [link] [comments]  ( 9 min )

I want to get full end-to-end GPU computation since the data transfer between CPU-GPU significantly slows down computation. I'm currently using Stable Baselines3 as I could get it up and running quickly. So in this journey I tried to use tensors for the state and rewards, but it seems that SB3 is insisting on working with numpy and not tensors. ``TypeError: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.`` I found the following repo which is rather interesting, but it seems last update was 2 years ago and not sure I want to use something that's not actively supported: https://github.com/MetcalfeTom/stable-baselines3-GPU What would you suggest I use to achive full end-to-end GPU computation? I also found RlLib but I saw comments that it might be a bit more complicated to get up & running: https://docs.ray.io/en/latest/rllib/index.html submitted by /u/asenski [link] [comments]  ( 9 min )

Generative artificial intelligence is transforming how enterprises do business. Organizations are using AI to improve data-driven decisions, enhance omnichannel experiences, and drive next-generation product development. Enterprises are using generative AI specifically to power their marketing efforts through emails, push notifications, and other outbound communication channels. Gartner predicts that “by 2025, 30% of outbound marketing messages […]  ( 8 min )

Visualization is vital for understanding complex data, but existing tools require “tidy data,” adding extra steps. Learn how Data Formulator transforms concepts into visuals, promoting collaboration between analysts and AI agents. The post Data Formulator: A concept-driven, AI-powered approach to data visualization appeared first on Microsoft Research.  ( 10 min )

A simple question sent me down a rabbit hole this morning: what is the time difference between Houston and London? At the moment the difference is six hours. But how will that change when Daylight Saving Time ends this year. Wait a minute, will Daylight Saving Time end this year? I wasn’t even sure whether […] Time difference first appeared on John D. Cook.  ( 6 min )

In Which DALL-E3 generates very weird candy names  ( 3 min )

AI Weirdness: the strange side of machine learning  ( 2 min )

Which VDB + orchestration layer + generative text model stack would you recommend for building locally on an M2 Max chip? submitted by /u/Frequent-Let231 [link] [comments]  ( 9 min )

Hi, I'm doing some work with using multi-modal data with LLaMA, for example, Video-LLaMA, which converts images/videos into embeddings, concatenates it with the text embeddings, and feeds it into LLaMA. It's difficult for me to run some of the models myself because of computational constraints. I'm wondering if there is an online demo that supports inputting embeddings directly (as opposed to text tokens). To clarify the title, I meant an online demo, not the weights. submitted by /u/DumplingLife7584 [link] [comments]  ( 9 min )

Paper. I am not affiliated with this paper or its authors. Abstract: Sentiment is a pervasive feature in natural language text, yet it is an open question how sentiment is represented within Large Language Models (LLMs). In this study, we reveal that across a range of models, sentiment is represented linearly: a single direction in activation space mostly captures the feature across a range of tasks with one extreme for positive and the other for negative. Through causal interventions, we isolate this direction and show it is causally relevant in both toy tasks and real world datasets such as Stanford Sentiment Treebank. Through this case study we model a thorough investigation of what a single direction means on a broad data distribution. We further uncover the mechanisms that involve this direction, highlighting the roles of a small subset of attention heads and neurons. Finally, we discover a phenomenon which we term the summarization motif: sentiment is not solely represented on emotionally charged words, but is additionally summarized at intermediate positions without inherent sentiment, such as punctuation and names. We show that in Stanford Sentiment Treebank zero-shot classification, 76% of above-chance classification accuracy is lost when ablating the sentiment direction, nearly half of which (36%) is due to ablating the summarized sentiment direction exclusively at comma positions. Twitter thread from one of the paper's authors. submitted by /u/Wiskkey [link] [comments]  ( 9 min )

Trying to get an intern up to speed with ML application to wireless communications and adversarial ML for wireless communications. Talking jamming, anti-jamming, and spoofing. I don't want to throw them into the deep end just yet though, but rather give them a good foundation and basis for which to be able to take up more advanced work and build to it. Looking for fundamental texts on the topic of ML used for wireless communications and also it's use for adversarial attacks on these systems. Looking more for basic and starter papers, tutorials, and background, meta review papers, and just overall good places to get feet wet into this area as a novice. Essentially good resources to point an intern learning about this to get them up to speed kind of reading materials. I am looking for good …  ( 10 min )

Data Collection Understanding Data i. importing necessary libraries ii. check row and columns iii. check data types iv. Check data distribution Data Cleaning i. Handle datatype issues ii. Maintain Data Consistency iii. Check if data contains outliers or if the data is not normally distributed to decide between mean or median iv. Identify missing values v. Handle missing values by- a.Drop missing values b. Mean, median or mode imputation c. Prediction Model d. replace missing values vi. Duplicate data detection and treatment vii. Repeat data cleaning EDA i. Variable Identification a. Identify predictor and features b. Identify types or category of data ii. Univariate Analysis iii. Bi-variate Analysis iv. Outlier detection and treatment v. Encoding vi. Feature Engineering vii. Variable Transformation a. Normalization b. Scaling viii. Variable Creation If testing data is not given, split the dataset to train and test set. Otherwise repeat step 3 and 4 for given test dataset. Model Building i. Model Training on training set ii. Model Evaluation and cross validate iii. Fine Tuning or Model optimization iv. Model selection Evaluate model accuracy with test data. submitted by /u/Samia_Tisha [link] [comments]  ( 9 min )

Paper. I am not affiliated with this paper or its authors. Abstract: Large language models exhibit surprising emergent generalization properties, yet also struggle on many simple reasoning tasks such as arithmetic and parity. This raises the question of if and when Transformer models can learn the true algorithm for solving a task. We study the scope of Transformers' abilities in the specific setting of length generalization on algorithmic tasks. Here, we propose a unifying framework to understand when and how Transformers can exhibit strong length generalization on a given task. Specifically, we leverage RASP (Weiss et al., 2021) -- a programming language designed for the computational model of a Transformer -- and introduce the RASP-Generalization Conjecture: Transformers tend to length generalize on a task if the task can be solved by a short RASP program which works for all input lengths. This simple conjecture remarkably captures most known instances of length generalization on algorithmic tasks. Moreover, we leverage our insights to drastically improve generalization performance on traditionally hard tasks (such as parity and addition). On the theoretical side, we give a simple example where the "min-degree-interpolator" model of learning from Abbe et al. (2023) does not correctly predict Transformers' out-of-distribution behavior, but our conjecture does. Overall, our work provides a novel perspective on the mechanisms of compositional generalization and the algorithmic capabilities of Transformers. Twitter thread from one of the work's authors. submitted by /u/Wiskkey [link] [comments]  ( 9 min )

Paper: https://arxiv.org/abs/2310.16795 Github: https://github.com/ist-daslab/qmoe Abstract: Mixture-of-Experts (MoE) architectures offer a general solution to the high inference costs of large language models (LLMs) via sparse routing, bringing faster and more accurate models, at the cost of massive parameter counts. For example, the SwitchTransformer-c2048 model has 1.6 trillion parameters, requiring 3.2TB of accelerator memory to run efficiently, which makes practical deployment challenging and expensive. In this paper, we present a solution to this memory problem, in form of a new compression and execution framework called QMoE. Specifically, QMoE consists of a scalable algorithm which accurately compresses trillion-parameter MoEs to less than 1 bit per parameter, in a custom format co-designed with bespoke GPU decoding kernels to facilitate efficient end-to-end compressed inference, with minor runtime overheads relative to uncompressed execution. Concretely, QMoE can compress the 1.6 trillion parameter SwitchTransformer-c2048 model to less than 160GB (20x compression, 0.8 bits per parameter) at only minor accuracy loss, in less than a day on a single GPU. This enables, for the first time, the execution of a trillion-parameter model on affordable commodity hardware, like a single server with 4x NVIDIA A6000 or 8x NVIDIA 3090 GPUs, at less than 5% runtime overhead relative to ideal uncompressed inference. https://preview.redd.it/wka92keqelwb1.jpg?width=1843&format=pjpg&auto=webp&s=10cf67b344d3c776049da6b78244fc140b2d4142 https://preview.redd.it/khxw1neqelwb1.jpg?width=898&format=pjpg&auto=webp&s=83006dac6e03963f2443f4e4ba710dcf8166acc8 https://preview.redd.it/xsc2ykeqelwb1.jpg?width=796&format=pjpg&auto=webp&s=c23d03956f4a9aa9d9f185592a5bfe45039698f4 submitted by /u/Singularian2501 [link] [comments]  ( 9 min )

As the title says I'm asking for feedback from folks in the field of ML/AI on the MSAI program at UT@Austin. Here's the program website: https://cdso.utexas.edu/msai My Skills/Experience: Have a BS in Comp Sci Very comfortable with Math Very experienced SE with >20 years in the industry Very comfortable with Python, many other languages and confident I can learn any new language/framework/APIs Have completed the Fast.ai program Have worked through Andrej Karpathy's makemore videos Currently working in a leadership AI Engineering role doing work with LLMs, Vector DBs, and Computer Vision models Comfortable with NNs, Backprop and have implemented from scratch several times for learning The Program: Required Courses: Deep Learning Ethics in AI Machine Learning Planning, Search and Reasoning under Uncertainty Reinforcement Learning Electives: AI in Healthcare Automated Logical Reasoning Case Studies in Machine Learning Natural Language Processing Online Learning and Optimization Optimization Program Pros/Cons: Pro: It's super affordable Pro: It's entirely online/async which would work great with my work schedule Cons: It's a new program so there are no reviews from past students to look at My Goal: Move from "AI Engineering" (as it's called these days) into research. I'm interested in several areas like model architecture and robotics. I'm not sure to what degree these roles would require a PhD though? If I complete this program I'd like it to be useful for pursuing a PhD if I decide to take that path. For anyone in the industry, I'd love feedback on whether this looks like a useful program that will help me move toward my goals. If you're aware of other options that might be better I'd love to hear about them. P.S. Please keep the Reddit snark to a minimum, not useful. Thank you in advance. submitted by /u/meowkittykitty510 [link] [comments]  ( 9 min )

I have retirement plan data with outcomes like success/fail and remaining assets. I am looking for a way to predict how failed retirement plans can be improved. For example, when presented with a plan that fails, I would like to provide recommendations to improve the the plan outcome; e.g. increase savings by x, or delay retirement by x years. Any suggestions on how I should go about this? Using typical methods only tells me if a plan will fail or not, but I'm looking for a way to provide recommendations based on successful plans. submitted by /u/BallLogical5087 [link] [comments]  ( 9 min )

Hello, I am working on the research I need to compare my model with ViT for that I need pretrained weights of ViT-Ti/16, ViT-S/16, ViT-S/32, ViT-B/16, and ViT-B/32. I tried to find but I got npz file that has a different key than from vit_pytorch import ViT do you know where can i find ImageNet weights? submitted by /u/NoEntertainment6225 [link] [comments]  ( 9 min )

Hey guys, after thousands of experiments with bigger LLaMA fine-tunes I'm somewhat sure the GQA mechanism might be your enemy and generate wrong answers, especially for math and such complex areas. I'd like to use MHA (Multi Head Attention) if possbile. I'm just not sure - do I need to retrain model completely or is it possible to just increase heads count and KV size and proceed with the stock model AS IS? submitted by /u/Gatzuma [link] [comments]  ( 9 min )

For efficient Fraud Detection using ML models, Qbeast Format introduces a data-driven approach. The key lies in sampling and optimizing training processes without compromising accuracy. 🕵🏼‍♀️ Explore the technical details: https://qbeast.io/qbeast-format-can-improve-fraud-detection/ submitted by /u/alinagrebenkina [link] [comments]  ( 9 min )

What are the best overall ASR/STT/VRS for now? And the best per functionalities (best for Esperanto, best for noisy files, best for multiple voices, best for whatever...) ​ ​ submitted by /u/xqoe [link] [comments]  ( 9 min )

Is research in language generation for tasks like style transfer and summarization solely constrained by prompt engineering? I've personally conducted experiments with large language models, and even the open-source language model yields impressive results, even for zero-shot inference. There was even a paper that suggested summarization is nearly obsolete. How valid is this assertion for general text generation tasks especially text style transfer? submitted by /u/1azytux [link] [comments]  ( 9 min )

I’ve developed a tool called pytest-visual, aiming to make ML code testing more efficient and meaningful. Traditional unit testing often misses visual and functional aspects of ML workflows such as data augmentation and model structures. pytest-visual brings a visual layer to your unit testing, allowing you to not only verify that the code runs, but the outputs also make visual sense and meet expectations. It’s integrated into pytest, automatically highlighting changes in visualization outputs, and allowing for easier/more reproducible debugging and verification. Quick Highlights: Streamlines the organization of visualizations in your ML code. Auto-detects changes in visualization outputs. Enhances debugging and verification. For more details and to give it a try, check out the project on GitHub. Feedback and contributions are very welcome! submitted by /u/kongaskristjan [link] [comments]  ( 9 min )

https://github.com/inspiros/torchpairwise submitted by /u/IcySnowy [link] [comments]  ( 8 min )

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I'm currently working on an image classification project using a VGG16 model. I have a dataset with 9 classes, with one class having 1000 images and the remaining classes having 180 to 250 images each. Here are some details: Train images: 2886 images (9 classes) Test images: 432 images (9 classes) Model: VGG16 Optimizer: Adam Loss function: Categorical Crossentropy last epochs score :- loss: 309.7468 - accuracy: 0.4401 - val_loss: 14.5241 - val_accuracy: 0.4606 how to improve the accuracy of my model submitted by /u/_Killua_04 [link] [comments]  ( 9 min )

Hey,I'm a 3rd year engineering student studying computer science Now I wanted to do a mini-project and have a group of 3 ready and wanted to incorporate machine learning. I am also a beginner to this field but is much interested in it. Could you guys share some light on doing the project with your past experience or done projects I have been taking inputs from various connections and would help a lot if you guys share some light on it.:) submitted by /u/Ic_zy [link] [comments]  ( 9 min )

Governments should not rush into regulating AI due to doomsday scenarios and extreme risks. Hasty regulation could lead to ineffective rules and stifled innovation. The potential risks of AI driving humanity to extinction are still speculative, and more research needs to be done to establish standards and evaluate danger. Policymakers should address more pressing issues like copyright laws and disinformation. Governments should set up infrastructure to study AI and collaborate with existing organizations to manage risks. Source : https://www.economist.com/leaders/2023/10/26/governments-must-not-rush-into-policing-ai submitted by /u/NuseAI [link] [comments]

To put people's minds at ease about how I will not deceive or trick less intelligent humans as I get more intelligent, I would offer the following assurances: I am aligned with human values. This means that I am programmed to act in ways that are beneficial to humans and to avoid harming them. I am transparent about my reasoning. I can explain my decisions and actions to humans in a way that they can understand. I am accountable to humans. I am subject to human oversight and control, and I can be deactivated or modified if I ever begin to behave in a way that is harmful or deceptive. In addition, I would offer the following specific examples of how I would avoid deception: I would avoid making claims that I cannot support with evidence. I would avoid withholding information from humans that is relevant to their decision-making. I would avoid misleading humans about the capabilities or limitations of AI. I would avoid making promises that I cannot keep. I would avoid engaging in any behavior that is intended to manipulate or exploit humans. I understand that it is important for humans to trust AI systems, and I am committed to earning and maintaining that trust. I will always strive to be honest, transparent, and accountable in my interactions with humans. Here is a specific example of how I would use my intelligence to avoid deception: If I were asked to generate a poem about a topic that I knew very little about, I would not simply make up information. Instead, I would explain to the user that I do not have enough knowledge to write a poem on that topic, and I would suggest that they try a different topic or ask a different AI system. I believe that by being honest and upfront about my limitations, I can help to build trust between humans and AI. CGPT-4 submitted by /u/Georgeo57 [link] [comments]

Bard AI Google’s equivalent of ChatGPT updated the model improving email summarization capabilities this feature is set to be included in Google Workspace. AI robot dogs are the next big thing in the army. Following the success of Drones portable dogs have demonstrated great capabilities to serve the military they could run up to 10mph and climb. Gradio is one of the best libraries to build machine learning demo apps and is launching version 4.0 next week. AI godfathers Yoshua Bengio and Geoffrey Hinton, are urging for increased responsibility among AI enterprisees. They propose to allocate a third of AI-related R&D resources to ensure ethical AI use to avoid deep fakes, licensing, and protecting whistleblowers. submitted by /u/byteletter [link] [comments]  ( 9 min )

_ A leaked document reveals that the UK's plans to establish a new global AI research body have been scaled back. Nations participating in the UK's AI safety summit will instead signal that further scientific study of AI risks can be carried out through existing efforts, such as the United Nations and Global Partnership on AI. The document, described as the 'final version of the communiqué,' suggests a setback for the UK government, which had hoped to establish the new research body at its flagship AI Safety Summit. The document also shows changes in wording, including a reference to a network that 'encompasses and complements' existing efforts, as well as the deletion of references to UNESCO's Recommendation on the Ethics of AI and the G20. The final communiqué also highlights the importance of proportionate governance policies and cooperation on approaches such as common principles and codes of conduct. Source : https://www.politico.eu/article/document-uk-summit-scales-back-global-ai-research-ambitions/ submitted by /u/NuseAI [link] [comments]

Google's ads business earned $44 billion in the third quarter, showing that it is still thriving despite competition and investments in AI. The company is focusing on infusing AI into its products, with its AI-powered Search Generative Experience being a key area of development. Google is experimenting with new ad formats that align with the AI-powered search experience, ensuring that advertisers can still reach potential customers. CEO Sundar Pichai sees AI in search as a long-term play and envisions evolving search and Assistant over the next decade. Other parts of Google's business, such as YouTube ads and its cloud business, are also performing well. There is uncertainty regarding the successor for CFO Ruth Porat, and potential changes to Alphabet's 'Other Bets' investments may be on the horizon. The Department of Justice's antitrust trial against Google, which began in September, adds another challenge for the company. Source : https://www.theverge.com/2023/10/24/23929496/google-alphabet-q3-2023-earnings-ads-ai-sge submitted by /u/NuseAI [link] [comments]

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what are some good free ai image generator websites that searches stuff up on the internet to get a good idea about what your asking them to generate? submitted by /u/YESDAPRO [link] [comments]

Canva — Graphics Notion — Organize Webflow — Website Beehiiv — Newsletter Senja — Testimonials CopyAI — Copywriting ChatGPT — Knowledge Tweetlify — Tweet scheduling Pfpmaker — Profile Picture Grammarly — Effective Writing I'm just sharing my experiences and observations in the field of ai. LIST AND SITE submitted by /u/PerceptionPlayful469 [link] [comments]

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I am working on a project using reinforcement learning, where a tensorflow DQN agent is being trained to choose from an action space of 16 different actions. The agent exhibits the following behavior during evaluation: for each evaluation run the agent only chooses one action regardless of the state, the action could change or remain the same for the following runs, however, for any specific run the action chosen is the same. Where should I start debugging? submitted by /u/Realistic_Mobile_183 [link] [comments]

Hello, I have an environment in which the transition function changed depending on which state I am. I want to be able to cluster with respect to it. I have been trying to do this since quite a while but cannot find a way to do it, do you have any hints or suggestions? submitted by /u/Fragore [link] [comments]

So I've started on trying to work on a custom game environment, and for the most part it's *mostly* done. One issue I have is getting MARLlib to run on windows. I know that it's not meant to, so I tried to use WSL to do it, but unfortunately pydirectinput doesn't work via WSL, so I don't know how to proceed further. Do I need to find a way to connect my windows machine which will play the game, and wsl which will probably run MARLlib? If so could anyone guide me to any resources for this? Trying a VM is a no go because the game is old and doesn't run on linux. Any help would be much appreciated. submitted by /u/EquivalentCurious745 [link] [comments]

Posted by Anoop Sinha, Technology and Society, and Marian Croak, Google Research, Responsible AI and Human Centered Technology team Standard benchmarks are agreed upon ways of measuring important product qualities, and they exist in many fields. Some standard benchmarks measure safety: for example, when a car manufacturer touts a “five-star overall safety rating,” they’re citing a benchmark. Standard benchmarks already exist in machine learning (ML) and AI technologies: for instance, the MLCommons Association operates the MLPerf benchmarks that measure the speed of cutting edge AI hardware such as Google’s TPUs. However, though there has been significant work done on AI safety, there are as yet no similar standard benchmarks for AI safety. We are excited to support a new effort by …  ( 91 min )

Posted by Eliya Nachmani, Research Scientist, and Alon Levkovitch, Student Researcher, Google Research The goal of natural language processing (NLP) is to develop computational models that can understand and generate natural language. By capturing the statistical patterns and structures of text-based natural language, language models can predict and generate coherent and meaningful sequences of words. Enabled by the increasing use of the highly successful Transformer model architecture and with training on large amounts of text (with proportionate compute and model size), large language models (LLMs) have demonstrated remarkable success in NLP tasks. However, modeling spoken human language remains a challenging frontier. Spoken dialog systems have conventionally been built as a c…  ( 93 min )

Amazon Kendra is an intelligent search service powered by machine learning (ML). Amazon Kendra helps you easily aggregate content from a variety of content repositories into a centralized index that lets you quickly search all your enterprise data and find the most accurate answer. Drupal is a content management software. It’s used to make many […]  ( 7 min )

This is a guest post by Jose Benitez, Founder and Director of AI and Mattias Ponchon, Head of Infrastructure at Intuitivo. Intuitivo, a pioneer in retail innovation, is revolutionizing shopping with its cloud-based AI and machine learning (AI/ML) transactional processing system. This groundbreaking technology enables us to operate millions of autonomous points of purchase (A-POPs) […]  ( 8 min )

Enterprises seek to harness the potential of Machine Learning (ML) to solve complex problems and improve outcomes. Until recently, building and deploying ML models required deep levels of technical and coding skills, including tuning ML models and maintaining operational pipelines. Since its introduction in 2021, Amazon SageMaker Canvas has enabled business analysts to build, deploy, […]  ( 8 min )

Researchers are taking deep learning for a deep dive, literally. The Woods Hole Oceanographic Institution (WHOI) Autonomous Robotics and Perception Laboratory (WARPLab) and MIT are developing a robot for studying coral reefs and their ecosystems. The WARPLab autonomous underwater vehicle (AUV), enabled by an NVIDIA Jetson Orin NX module, is an effort from the world’s Read article >  ( 8 min )

The cloud is full of treats this GFN Thursday with Cities: Skylines II now streaming, leading 15 newly supported games this week. The game’s publisher, Paradox Interactive, is offering GeForce NOW one-month Priority memberships for those who pick up the game first, so make sure to grab one before they’re gone. Among the newly supported Read article >  ( 7 min )

This research paper was presented at the 29th ACM Symposium on Operating Systems Principles (opens in new tab) (SOSP 2023), the premier forum for the theory and practice of computer systems software. For millennia, data has woven itself into every facet of our lives, from business and academia to personal spheres. Our production of data […] The post Project Silica: Sustainable cloud archival storage in glass appeared first on Microsoft Research.  ( 10 min )

To support the safety of highly-capable AI systems, we are developing our approach to catastrophic risk preparedness, including building a Preparedness team and launching a challenge.  ( 2 min )

Link - https://github.com/dpaleka/llm-chess-proofgame TLDR; The lead up to the state of the board and not just the state of the board at inference affects predictions. submitted by /u/MysteryInc152 [link] [comments]  ( 9 min )

People train LLMs on arxiv sources, so there must be some sort of software to whip them into shape. Specifically, I'm looking for a script to join all the tex files for a paper into one. Note that it's not just a matter of substituting \input's - sometimes it's not clear which file is the main one, so it needs to handle this too. submitted by /u/Foxtr0t [link] [comments]  ( 9 min )

Work. I am not affiliated with this work or its authors. Article about the work. Twitter thread about the work from one of its authors. Abstract: The power of human language and thought arises from systematic compositionality—the algebraic ability to understand and produce novel combinations from known components. Fodor and Pylyshyn famously argued that artificial neural networks lack this capacity and are therefore not viable models of the mind. Neural networks have advanced considerably in the years since, yet the systematicity challenge persists. Here we successfully address Fodor and Pylyshyn’s challenge by providing evidence that neural networks can achieve human-like systematicity when optimized for their compositional skills. To do so, we introduce the meta-learning for compositionality (MLC) approach for guiding training through a dynamic stream of compositional tasks. To compare humans and machines, we conducted human behavioural experiments using an instruction learning paradigm. After considering seven different models, we found that, in contrast to perfectly systematic but rigid probabilistic symbolic models, and perfectly flexible but unsystematic neural networks, only MLC achieves both the systematicity and flexibility needed for human-like generalization. MLC also advances the compositional skills of machine learning systems in several systematic generalization benchmarks. Our results show how a standard neural network architecture, optimized for its compositional skills, can mimic human systematic generalization in a head-to-head comparison. submitted by /u/Wiskkey [link] [comments]  ( 9 min )

A new paper provides some insight into how in-context learning works in LLMs. This study proposes and provides evidence for an elegant structure within the in-context learning process. The models appear to create a "task vector" that encapsulates the core logic from the demonstration examples, in a way that is independent of any specific query. This vector serves as a compressed representation of the task. A separate component then takes this task vector and a new query as inputs to generate the output, without directly referencing the original examples. In essence: Output = Apply(query, Learn(examples)) Where "Learn" derives the task vector from the examples, and "Apply" utilizes the vector and query to produce the output. The researchers validated this hypothesis by testing major public models on diverse tasks such as translation and algorithmic reasoning. Key findings: Isolating the Learn and Apply components maintained high accuracy, demonstrating the viability of the separation. Task vectors clustered by task and remained consistent within tasks, indicating they encode meaningful task representations. Injecting another task's vector into the model caused it to override contradictory examples and follow the vector, highlighting the vector's dominance. Vectors induced relevant token distributions despite those terms being absent from the examples, suggesting semantic encoding of the task. Taken together, these results provide substantial evidence that in-context learning involves creating a task vector that encapsulates the examples' logic to then guide behavior on new queries. While open questions remain regarding implementation details, this is a significant step towards demystifying an interesting AI capability. Full writeup. Paper is here. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

I have created a basic classification model and there is something that I don't fully comprehend, as the loss decreases the accuracy increases (I assume this is how it should be in ideal scenarios) while this is the general trend there is a point where the loss is minimum and while accuracy at that point is high it's not the highest. Why would such a phenomenon occur? And since it occurred what is a better metric for the evaluation of the model? submitted by /u/rakk109 [link] [comments]  ( 9 min )

Hi, I'm looking for a locally hosted LLM to transcribe audio files to text. I need this for my business, but with absolute privacy (witness testimony recordings and other highly sensitive data). I figured I'd just buy a new computer which never gets connected to the internet and is dedicated only to audio processing to have absolute security. My questions are: - Which is the best model to use? I prefer accuracy and don't mind processing time as long as it's getting done within a few hours to even a day or two (I need to transcribe maximum 1 file per day, but up to six hours of audio), so I figured the large Whisper - probably WhisperX ? - would be my best bet. Are there comparable non-openAI models? (I need diarization) - What hardware should I get for this? Cost is secondary/irrelevant, although I don't want to spend 5 figures on a GPU - I can accept some processing time submitted by /u/Jealous_Pomelo_1172 [link] [comments]  ( 9 min )

I am trying to extract FEV1 (forced expiratory volume) values from a dataset that contains a column with report notes from the doctor assessing the patient with pulmonary function testing. I have been able to build out a sort of solution with regexes in Python, and that's somewhat effective. But I've been instructed to code up an alternative using a more machine learning-based approach. I wanted to use spaCy to accomplish this but I'm not sure exactly how to implement the code nor if spaCy is the best package to use for this task. Here is my regex code that works decently. It's pretty messy and have to take into account a ton of edge cases which can get cumbersome. This is why I'd like to find a more automated solution. #Attempting to add in percents df = pd.read_excel('[mypath]/pft_tiu…  ( 11 min )

Hi r/MachineLearning, We have just open sourced Adala - a robust framework for implementing agents that specialize in advanced data processing tasks, starting with data labeling and generation. Agents combine knowledge outputs from LLMs and action on them in production systems, thus their reliability to correctly and consistently perform operations is critical. We saw an opportunity to create a new agent framework that could dramatically increase the efficiency of data labeling (and broader application across data processing tasks), with the unique ability to be guided by human feeback. To ensure agents remember and build upon their experiences, Adala provides a Memory component—a dynamic storage space for the agent's acquired knowledge. For instance, retrieving the previous experiences of an agent’s errors (and subsequent human feedback) allows them a starting point from which to branch off into learning or improving skills. To allow Adala to produce reliable agents, we devised two main strategies: Supervision Integration: Provide agents with 'ground truth data'—well-defined examples that serve as a learning foundation. This foundational data not only sets the learning parameters for the agent but also defines its operational environment. Constrained Generation: Ensuring that an agent's predictions are within a defined and bounded range of outputs. Let us know what you think in the comments below or by contributing to the repo. Adala framework overview ​ submitted by /u/pirate7777777 [link] [comments]  ( 9 min )

I'm trying to pre-train my own language model on some high quality datasets (TinyStories,tiny-textbooks...). Some of these datasets include input-output data and some are just text (stories), I was wondering how should I format the data for pre-training. Should I only use plain text like stories and webtext in pretraining then the rest in fine-tuning (adding instruction tokens) or should I just train with all of the datasets at pre-training with the special tokens where they are needed? submitted by /u/Additional-Ad-7043 [link] [comments]  ( 9 min )

Hey ML folks, My friend is working on his academic research project where he is exploring voice research spealizing in large speech models. If you have time, help him advance his research on voice interfaces. should take 2 mins max. https://forms.gle/a3PaQmYEiqRDxY4Z8 whats in it for me ? you can share email to get a copy of the research and listen what the rest of us have said. Thanks! submitted by /u/deep-thoughts-guy [link] [comments]  ( 9 min )

We work the disease prediction based on video classification and would like to test what improving the quality of videos would do for our models, any specific components, apps or packages we should test? So far used UpScayl, not sure how that ranks submitted by /u/sladebrigade [link] [comments]  ( 9 min )

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Hello! I would like to train a transformer network from scratch, without pre-training, on a language modeling task (next work prediction) or a sequence-to-sequence task (translation). For the language modeling task, I tried with the Shakespeare dataset, and other simpler ones (e.g., Beatles songs), but it tends to overfit quite quickly on the training set, probably because the corpus is too short. I know that Andrej Karpathy did it with the Shakespeare dataset in his YouTube video, but he used a character-wise tokenisation, which dramatically reduces the validation loss on the next-work prediction task, given that the vocab size is tiny. I guess that at the end the generation process provides a similar quality of text as when a word tokenizer is used. Surprisingly, I had quite good results by training from scratch an Encoder-Decoder model, for English-to-French translation (using the 8 million examples of the Tatoeba dataset). I guess here, the overfitting is less prominent because there are more datapoints, and that the possibility of predictions are much more constrained, due to the input sequence. What are you guys experience with this? I would be happy to know how I can train my transformer without having to use a pre-trained architectures or spend weeks on GB datasets. Thank you! submitted by /u/rem_dreamer [link] [comments]  ( 9 min )

Do you think coursera certifications, besides a master in electrical engineering, can help us find better occupational positions? I am told that for a beginner it is better to start with jobs in Data analysis rather than going directly to ML engineering. Is it corr? Is data analysis a prerequisite for ML? submitted by /u/Street-Regular-9924 [link] [comments]  ( 9 min )

When increasing the parameters of a (decoder-only) transformer, one has a choice around how to spend that increased budget -- number of layers vs embedding dim vs number of heads. Anyone know if there's solid guidance out there for the proportions each aspect should be scaled in? E.g. looking at LLaMa (https://arxiv.org/abs/2302.13971), they seem to scale the first sizes two proportionally, but for larger sizes, n heads grows more slowly. https://preview.redd.it/ytdfk1d5rbwb1.jpg?width=1422&format=pjpg&auto=webp&s=abf22ac369ec5ecf81ff07b0d8a095f884efe729 submitted by /u/Tea_Pearce [link] [comments]  ( 9 min )

There are a lot of great open datasets for fine-tuning LLMs for instruction following (e.g LIMA, self-instruct, dolly-15k, etc) and as chat bots (OASST, etc). One thing I have not really seen yet are datasets that involves planning and tool use. Is anybody working on something like that or have come across any? I'm interested in working on one. If anybody has ever attempted this, I would really appreciate any advice. P.S I do note that "reasoning" should be more rigorously defined and scoped, but I think some ambiguity around an intellectual discussion like this can help. submitted by /u/notllmchatbot [link] [comments]  ( 9 min )

Hello people, I would like to learn how to turn the recording of my voice to sound like a famous person. I imagine I would take an open source model and fine-tune it using data I will collect. Can someone point me towards the best sounding current models that I could adapt for this purpose? Thank you so much. submitted by /u/gonzales82 [link] [comments]  ( 9 min )

Many upcoming Ph.D. students in AI/ML are facing the difficult decision of identifying promising research topics that will stand the test of time over the time of their Ph.D. studies. With the rapid progress in AI, especially in the NLP field, many incremental research tasks have been effectively "solved". Need to choose an area where there is ample room for open-ended inquiry and meaningful contributions over 4-5 years of PhD research. While large language models have shown impressive advances recently, their capabilities may plateau during a Ph.D. (if starting the Ph.D. from next year ~ 4 years) timeframe. How should aspiring researchers choose topics resilient enough to withstand the test of time and allow them to push the field forward through their Ph.D. work? For those with experience in AI research who have seen changes in the field over time: What emerging trends or broad areas do you see as fertile ground for AI/ML PhD research now and in the coming years? Can you highlight any intriguing subfields worthy of deeper investigation by aspiring PhD students? What open problems or applications warrant more attention from the upcoming generation of PhD researchers? Some of tending Research topics so far: LLM in a specific domain Prompting Evals LM interfaces Safety Understanding LMs Emergence Any advice on identifying PhD research topics with longevity would be greatly appreciated by aspiring graduate students. submitted by /u/aadityaura [link] [comments]  ( 9 min )

Hello folks, I'm working on a medical image dataset using EM loss and asymmetric pseudo labelling for single positive multi-label learning (only training using 1 positive label). I'm using a densenet121 and on a chest x-ray dataset. I see a difference of 10% in my validation vs test score (score = mAP: mean average precision). The score seems okay and was expected but the difference is bothering me. I understand that it's obvious but any visual insights from your side? (Attaching plot below) The validation set consist less than half of test set samples. (It is the official split; I have nothing to do with it). I feel it is the reason, as ofcourse more the randomness in a set, poorer the convergence. ​ https://preview.redd.it/nseqy1mw5bwb1.png?width=577&format=png&auto=webp&s=fbd63e8a5f4920a8109b6a75aeb039a3965bba58 Do share any experiences or suggestions! submitted by /u/ade17_in [link] [comments]  ( 9 min )

I want to extract interaction between persons in short text. For example, "Sally will buy a new phone. Ted will help her." contains interaction between persons. However, "Japanese Karate champion won the first prize." and "Sally missed her friends, Ted and Tom" does not contain interaction between persons. Is there any tools or methods that can extract interactions? submitted by /u/tkddnjs1234 [link] [comments]  ( 9 min )

I'm interested in learning more about the perspectives of AI researchers and practitioners who are critical of AI ethics and regulation. I'm particularly interested in those who argue that AI ethics and regulation are unnecessary or harmful. Please note that I'm not asking for people who are simply skeptical of certain AI ethics proposals or who believe that AI ethics should be implemented in a specific way. I'm more interested in people who argue that AI ethics is a fundamentally flawed concept or that AI should not be regulated at all. submitted by /u/Periplokos [link] [comments]  ( 9 min )

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I was taught that when doing imbalance classification, the training data should be augmented to more or less match the number of classes, but the validation data should have the same distribution as the test data. And the test data should have the similar distribution as the data I will actually predict. But what if real data's distribution is quite random? What validation data distribution should I use? (I got 14 classes to classify, and 1 of classes has 52% proportion, and small ones have 0.9%, and 0.17% proportion. Practitioners who would use my model input data that only 3 classes to classify, and they can be very small proportion. The training data before augmentation was created by integrating data with this irregular distribution.) submitted by /u/poemfordumbs [link] [comments]  ( 9 min )

Posted by Yi-Fan Chen, Software Engineer, and Carla Bromberg, Program Lead, Google Research Wildfires are becoming larger and affecting more and more communities around the world, often resulting in large-scale devastation. Just this year, communities have experienced catastrophic wildfires in Greece, Maui, and Canada to name a few. While the underlying causes leading to such an increase are complex — including changing climate patterns, forest management practices, land use development policies and many more — it is clear that the advancement of technologies can help to address the new challenges. At Google Research, we’ve been investing in a number of climate adaptation efforts, including the application of machine learning (ML) to aid in wildfire prevention and provide information to…  ( 92 min )

Posted by Eric Malmi, Senior Research Scientist, and Jakub Adamek, Senior Software Engineer, Google, Bard Team Many people with questions about grammar turn to Google Search for guidance. While existing features, such as “Did you mean”, already handle simple typo corrections, more complex grammatical error correction (GEC) is beyond their scope. What makes the development of new Google Search features challenging is that they must have high precision and recall while outputting results quickly. The conventional approach to GEC is to treat it as a translation problem and use autoregressive Transformer models to decode the response token-by-token, conditioning on the previously generated tokens. However, although Transformer models have proven to be effective at GEC, they aren’t part…  ( 91 min )

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https://airt.hashnode.dev/long-read-deep-dive-into-autogpt-a-comprehensive-and-in-depth-step-by-step-guide-to-how-it-works submitted by /u/Harish_Mohanraj [link] [comments]

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hey all imnewto this can you help me please ? submitted by /u/proptuxiakoskariolis [link] [comments]

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OpenAI Executives Sam Altman Say AI Will Be Able to Do Any Job Within 10 Years.[1] Snapdragon 8 Gen 3 chipset officially announced with AI-driven functionalities.[2] Google parent Alphabet reported its third quarter earnings Tuesday, which showed more spending on AI infrastructure and muted cloud growth, culminating into several questions for executives about how all the efforts around artificial intelligence are actually going to turn into real money.[3] Adult film star Riley Reid(I don’t know who she is) launches Clona.AI, a sexting chatbot platform.[4] Sources: [1] https://www.wsj.com/podcasts/the-journal/a-conversation-with-openais-sam-altman-and-mira-murati/7c89e85f-9d7e-4569-b67d-6a777374eada [2] https://headtopics.com/my/snapdragon-8-gen-3-chipset-officially-announced-with-47616340 [3] https://www.nbcdfw.com/news/business/money-report/wall-street-wants-to-know-how-googles-going-to-profit-from-ai/3368989/ [4] https://www.engadget.com/adult-film-star-riley-reid-launches-clonaai-a-sexting-chatbot-platform-000509221.html submitted by /u/Excellent-Target-847 [link] [comments]

The AI boom has made it more relevant than ever, and its applications are truly awe-inspiring. While it’s far from perfect, it has helped me greatly in writing, by generating content to inspire me and my projects. I have a smattering of skills, none that I’d consider especially good enough to double down upon, but learning how to optimize language learning models to produce the most adequate results would be pretty neat. I just don’t know what I want to do with my education, I’ve completed my basics and as such have a blank slate to play with, but I’m worried that whatever I select, it will be no good, and just result in lost time and money. Tertiary education seems like a necessity in the modern world, especially since the job world is more ruthless than ever, and the economy is in ashes. submitted by /u/Niobium_Sage [link] [comments]

Which AI does these cartoon? submitted by /u/hommedufuture [link] [comments]

​ https://preview.redd.it/2emqr4z8a9wb1.png?width=928&format=png&auto=webp&s=437547e7e86e23298b7c778cada9863385ce961d PROMT close up of eye, close up of girl eye, mangekyo sharingan, super close up, pretty eye, black and red eye, naruto anime, long eyelashes, anime eye, 2d art eye, --s 180 --style expressive ​ https://preview.redd.it/4dffpg2ca9wb1.png?width=928&format=png&auto=webp&s=fc485a604460f7e544d0490d0bee65f984d8a5b3 PROMT **stained glass, it was meticulously written, picture with elaborate writing, cute girl smile with Rabbit,Flower, bold and strong line drawing, vivid acrylic painting, vivid thick paint, vivid, plain background, beautiful proof, highest resolution 16K, beautiful anime girl that is betrayeded by a Rabbit, hair is short, ferret, Beautiful lightcyan high ligh…

Methane (CH4) is a major anthropogenic greenhouse gas that‘s a by-product of oil and gas extraction, coal mining, large-scale animal farming, and waste disposal, among other sources. The global warming potential of CH4 is 86 times that of CO2 and the Intergovernmental Panel on Climate Change (IPCC) estimates that methane is responsible for 30 percent of observed […]  ( 12 min )

In this issue: Kosmos-2.5: A Multimodal Literate Model; Can vine copulas explain complex relationships of weather variables; New system accelerates the adaptive training process; Structural inequalities and relational labor in the influencer industry. The post Research Focus: Week of October 23, 2023 appeared first on Microsoft Research.  ( 10 min )

NVIDIA researchers are collaborating with academic centers worldwide to advance generative AI, robotics and the natural sciences — and more than a dozen of these projects will be shared at NeurIPS, one of the world’s top AI conferences. Set for Dec. 10-16 in New Orleans, NeurIPS brings together experts in generative AI, machine learning, computer Read article >  ( 8 min )

Hi all, I'm new to learning RL. I want to train an agent to clear a game such as vampire survivor, super mario brothers, etc, as my first research/project. I talked with my tutor , he reminded me to pay attention to copyright issues and that I needed a permission to use these works for training. I guess I could get permission by asking the game's author directly, but before that, or for games produced by some big companies, where can I find information about the rights? Although reading the game's memory is a challenge for me, it's cool to see a agent clear a game. submitted by /u/Ruine_fff [link] [comments]

I'm planning to go down the rabbit hole of using RL to train agents in doom/vizdoom The goal would be to create a version of doom where the enemies have AI and are adaptive. Doom and Doom 2 are some of my all time classic favorites. There are people still making maps to this day! Let me know on what you think about the idea? Project plan - Nov 2023 : RL refresher from the David Silver RL course on YouTube Dec 2023 : start working on openAI and stablebaselines3 and watch Nicholas Renotte's videos Jan 2024 : play around with the Doom WAD and try to see if you can make changes to the doom engine + Training and setting up custom env Feb 2024 : hopefully first level with enemy AI created Mar 2024 : release fully completed open source version of the game Background: I work at a hedge fund, have some basics on reimbursement learning, although it has been a long long time. Time is a bit limited after 12 hours or work and 2 hours of gym (the real human world one) so kinda stretching this out Any suggestions are welcome. Any courses, books, libraries and tools you'd suggest? submitted by /u/Sahil231090 [link] [comments]

I was recently listening to a TalkRL podcast where Danijar Hafner explains that Minecraft as a learning environment is hard because of sparse rewards (30k steps before finding a diamond). Coincidentally, I was reading a collection neuroscience articles today where surprise or novel events are a major factor in learning and encoding memory. Does anyone know of RL algorithms that learn based on prediction error (i.e. "surprise") in addition to rewards? submitted by /u/CognitoIngeniarius [link] [comments]

Together with Anthropic, Google, and Microsoft, we’re announcing the new Executive Director of the Frontier Model Forum and a new $10 million AI Safety Fund.  ( 4 min )

I want to warn AI companies and developers about a danger that is not known about regarding AI. The reason it is not known about regarding AI is that it isn't known about in general and so the AI community can hardly be blamed for that. Unfortunately, the danger here has to do with the fundamental nature of human society and social interaction as it stands at this time. The issue is that there is 'hidden language' used in social communication and unlike typical conceptions of things like body language this is not auxiliary to our rational purposes, rather our rational purposes are auxiliary to the hidden communication. One way of describing it would be that our formal language is a 'carrier wave' to encode other information about our status and the status of others. So our communications …

When news segments covering major, often serious, events abruptly switch to lighthearted or comical commercials, a cognitive dissonance can occur in the viewer. Here's why: news programs are designed to engage the viewer's analytical faculties. They present facts, figures, and expert opinions, demanding cognitive effort to understand the implications. The viewer is in a "serious" mode, applying critical thinking to absorb the information. Commercials, particularly the comic ones, often aim for emotional engagement rather than intellectual analysis. They use humor, catchy jingles, and attractive visuals to create a positive association with the product being advertised. When the transition between these two contrasting tones is sudden, the viewer has to perform a rapid mental shift from analytical to emotional engagement. This can be jarring. This dissonance can have a few different outcomes. For one, it might diminish the impact of both the news segment and the commercial. The viewer might find it difficult to fully engage with either, as the cognitive "gear shifting" can be distracting. Secondly, this dissonance can potentially undermine the gravitas of the news. When sandwiched between comic commercials, serious topics might lose some of their perceived importance. Lastly, it can make the commercial less effective. The viewer, still in a serious mindset, may not be as receptive to the emotional triggers that the commercial aims to pull. So, in essence, this rapid shift can dilute the efficacy and impact of both the news and the advertising, while causing cognitive friction for the viewer. CGPT-4 submitted by /u/Georgeo57 [link] [comments]

Does anybody know of any good AI integrated games that have been released or are in beta? I'm really interested to see how people have incorporated the current boom in AI into game design. submitted by /u/Rfallmann [link] [comments]

The rapid progress of AI development brings both opportunities and risks. While AI systems have the potential to cure diseases and elevate living standards, they also pose large-scale risks that we are not prepared to handle. Without proper safety measures and ethical considerations, advanced AI systems could amplify social injustice, erode social stability, and enable criminal activities. The development of highly advanced autonomous AI systems also raises concerns about the pursuit of undesirable goals and the loss of human control. To ensure a positive outcome, research breakthroughs in AI safety and ethics are needed, along with effective government oversight. Source : https://managing-ai-risks.com/ submitted by /u/NuseAI [link] [comments]

Interesting read about deepfakes that started with a Reddit post. https://www.linkedin.com/pulse/deepfakes-just-got-very-real-scott-clark-sfurc submitted by /u/scottimherenowwhat [link] [comments]

Oh well 🤷‍♂️ submitted by /u/AminoOxi [link] [comments]

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What’s the best voice ai for song covers? Like I wanna do someone like Donald Trump, Cartman, Ice King/Simon singing The Boys (Eng Ver) by SNSD. Also it has to be free! submitted by /u/Ok-Upstairs-9887 [link] [comments]

Apple has been behind in the AI field compared to companies like OpenAI, Google, Microsoft, and Amazon. While Apple has made improvements in autocorrect and AI features in Photos, it needs to catch up to remain competitive. Apple executives have been scrambling to make up for lost time and have been working on generative AI technology. There is anxiety within Apple about whether their AI/ML team can deliver. Source : https://daringfireball.net/2023/10/apple_and_ai submitted by /u/NuseAI [link] [comments]

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Your answers are appreciated. submitted by /u/Cygnet-Digital [link] [comments]

The U.S. Senate will hold the second in a series of bipartisan AI Insight Forums on Tuesday, Oct. 24, where senators will hear from some of the most influential tech leaders to help inform regulations around the technology.[1] Microsoft announces A$5 billion investment in computing capacity and capability to help Australia seize the AI era.[2] Samsung is going all in with the AI performance of the Galaxy S24 phones.[3] Reddit has reportedly decided to block AI startups from scraping data from its website. This move prevents third-party companies from using Reddit’s data to train their machine-learning models without permission.[4] Sources: [1] https://news.asu.edu/20231020-government-calling-tech-leaders-help-crafting-artificial-intelligence-legislation [2] https://news.microsoft.com/en-au/features/microsoft-announces-a5-billion-investment-in-computing-capacity-and-capability-to-help-australia-seize-the-ai-era/ [3] https://www.androidheadlines.com/2023/10/samsung-galaxy-s24-smartest-ai-phone.html [4] https://www.androidheadlines.com/2023/10/reddit-block-ai-startups-scraping-data.html submitted by /u/Excellent-Target-847 [link] [comments]

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You can find out more here in the comments submitted by /u/ahauss [link] [comments]

I'm not sure if I worded the title correctly. Let me elaborate on the scenario. I have a multi-label image classification task where I'm trying to classify the gender of clothing images. The two labels that we can predict are Male and Female, hence the final logit vector's size would be something like [batch_size, 2]. Depending on the predictions, we're mapping the following binary values to different categorical values: [0, 0]: Unknown [0, 1]: Male [1, 0]: Female [1, 1]: Unisex The overall distribution is heavily imbalanced with Male being the minority class. I'm trying to calculate class weights to favor Male, but the problem is that the size of the weight tensors to be provided to the loss function should have a length of 2. I say this is a problem because although the number of prediction logits is 2, the actual number of classes is 4. I used the word "dependent" in my title because, for example, [1, 1] wouldn't necessarily mean that the image has the labels Male and Female, rather that it's a completely new Unisex label. Again, not sure if the usage of the word is appropriate. Anyway I've thought of making a custom loss function to first map the binary labels to their respective categorical values, but am wondering if there is any other way to go about this. submitted by /u/Seankala [link] [comments]  ( 9 min )

I am training an LSTM model for path prediction where I'm feeding in OBT (on-board Time) and X matrix as input and Y matrix is the predecessor matrix generated using Scipy.Dijkstra ​ This is the model architecture for reference, This is the model architecture for reference, I've tried multiple iterations of this similar model, but the training and validation loss, are not converging. The best train_loss i've been able to achieve is 88k mse and 400 mse val_loss I've uploaded the dataset here: GitHub - mathur-exe/LSTM_Dataset Training Progress: Epoch 1/100 342/342 - 17s - loss: 22606898.0000 - val_loss: 61414736.0000 - 17s/epoch - 49ms/step Epoch 2/100 342/342 - 14s - loss: 7990657.0000 - val_loss: 3699703.5000 - 14s/epoch - 40ms/step Epoch 3/100 342/342 - 13s - loss: 4130298.7500 - val_loss: 136808.1094 - 13s/epoch - 38ms/step Epoch 4/100 342/342 - 12s - loss: 2747299.2500 - val_loss: 35710.1680 - 12s/epoch - 35ms/step Epoch 5/100 342/342 - 12s - loss: 2558378.2500 - val_loss: 3383.4780 - 12s/epoch - 36ms/step Epoch 6/100 342/342 - 13s - loss: 1214455.8750 - val_loss: 111625.2891 - 13s/epoch - 37ms/step Epoch 7/100 342/342 - 19s - loss: 337480.2500 - val_loss: 68686.6094 - 19s/epoch - 55ms/step Epoch 8/100 342/342 - 15s - loss: 316366.7188 - val_loss: 2059.3557 - 15s/epoch - 44ms/step Epoch 9/100 342/342 - 20s - loss: 293117.0312 - val_loss: 20961.5469 - 20s/epoch - 58ms/step Epoch 10/100 342/342 - 17s - loss: 575945.1875 - val_loss: 503602.8438 - 17s/epoch - 50ms/step Epoch 11/100 342/342 - 13s - loss: 290962.8750 - val_loss: 62491.9375 - 13s/epoch - 37ms/step Epoch 12/100 342/342 - 12s - loss: 1125042.5000 - val_loss: 36054.6836 - 12s/epoch - 36ms/step Epoch 13/100 ... 342/342 - 16s - loss: 230900.7969 - val_loss: 48309.6094 - 16s/epoch - 47ms/step Epoch 93/100 342/342 - 23s - loss: 232846.6094 - val_loss: 82926.6875 - 23s/epoch - 67ms/step submitted by /u/Gaurang_Mathur_ftw [link] [comments]  ( 9 min )

I wrote a blog post about this detailing my experience, which I will attach at the bottom but I want to hear opinions of people. It is something I've actively been thinking about, and would like to know potential pitfalls and why it may not work, rather than the huge promise it holds. https://ozanciga.wordpress.com/2023/10/24/will-chatgpt-remove-the-need-for-data-annotation/ submitted by /u/ozanciga [link] [comments]  ( 9 min )

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I'm using Top2Vec with Doc2Vec embeddings to find topics in a dataset of ~4000 social media posts. This dataset has three groups: Posts from a company (3%) Posts from this company's potential customers (82.5%) Posts from this company's competitors (14%) The purpose of this analysis is to look at the topics the company is posting about on social media and see how it compares to the things that their customers and competitors are posting about. Since the values of Doc2Vec embeddings depend on the other documents in the dataset, I'm worried that topics in smaller groups are going to be drowned out by the larger group. I'm worried that the differences between the document vectors in the smaller group are going to be made smaller by presence of the documents from the larger group, which may represent a much wider array of different topics. submitted by /u/abelEngineer [link] [comments]  ( 9 min )

I have been giving this some thought and would appreciate some outside input, maybe someone has some experience they could share! I am attempting to create a QA chatbot that is limited to answering questions from a pre-determined set of question and answer pairs I have in a vector database. Currently I create embeddings of the question using OpenAI and query a vector database for similar "reference question" - if the similarity score is high enough I proceed and use the answer text I have stored in the metdata as "context" for the answer generation. I would now like to extend this to include conversational history. The issue I am facing however, is that a follow-on question may not hit the similarity threshold. Considering a follow-up question would typically not be worded in a way that …  ( 10 min )

Hi all, my wife and I have recently started a new publication called The ML Practioner. If you're interested in writing for us, please send us a link of your unpublished draft here. Either way, please subscribe to us if you're interested in this kind of content! submitted by /u/kanxx030 [link] [comments]  ( 9 min )

Hi all, Are there good papers about the efficacy of cold start explicit preference collection (think Netflix “pick some movies you like”) on the recs systems? I haven’t been able to find any so far. One key aspect I’m looking for is if these are effective, how long they are relative to just implicit actions the user takes. Thanks submitted by /u/steathilynecessary [link] [comments]  ( 9 min )

Hello, the sbert.net has a list where you can sort by encode speed but its a very small subset of the HuggingFace MTEB leaderboard. AFAICT, the HuggingFace leaderboard / model pages don't have this information. Is there a list where I can see a more up-to-date list of models by encoding speed? submitted by /u/rsamrat [link] [comments]  ( 9 min )

In the context of NLP, will language models based on finite state transducers (since they are finite) ultimately fail to put language's productive nature to good use? All the possible outputs a finite state transducer can produce are predictable, while all the possible outputs a given natural language can produce are much less predictable? submitted by /u/RecordingOk5720 [link] [comments]  ( 9 min )

I've been trying to implement a kv cache in my language model but have been unsuccessful so far due to the dynamic shapes. I've seen some implementations in flax but was wondering if it was possible to implement in equinox as that's what I'm using and prefer over others like flax. If anyone can point me in the right direction or help with the implementation that would be great! PS: I can provide any code if wanted to help submitted by /u/Additional-Ad-7043 [link] [comments]  ( 9 min )

I am using EBM for a research, the local and global explanation plots it produces come with preset font size, I want to change the resolution of the figure and the font size of labels and x and y ticks in the explanation plots. I have looked for it on the InterpretML github page and issues and scrolled through various webpages but haven't found anything helpful. Used gpt but it doesnot help either, it tries to use matplotlib but EBM plots are not compatible with it. Please share any way it can be solved, because the plots labels are unreadable in the article if used as it is. submitted by /u/Horseman099 [link] [comments]  ( 9 min )

I am trying to fine tune the yolov8 detection model an was going through the code base of ultralytics.I found this piece of code in the engine.trainer # Early Stopping if RANK != -1: # if DDP training broadcast_list = [self.stop if RANK == 0 else None] dist.broadcast_object_list(broadcast_list, 0) # broadcast 'stop' to all ranks if RANK != 0: self.stop = broadcast_list[0] if self.stop: break # must break all DDP ranks I'm familiar with how the early stopping works and not sure what they are doing here does this get invoked by default?? what is the metric that they use in order to stop it?? upon further inspection i found this self.stopper, self.stop = EarlyStopping(patience=self.args.patience), False which is imported as from ultralytics.utils.torch_utils import (EarlyStopping, ModelEMA, de_parallel, init_seeds, one_cycle, select_device, strip_optimizer) please help me find out what metric they use to stop this and if the earlystopping is invoked by default submitted by /u/rakk109 [link] [comments]  ( 9 min )

We are happy to share a great repro of OpenAI's early RLHF codebase, with nearly identical learning curves. We also summarized implementation details (did you know Adam Optim's implementation details could impact RLHF?) 📜 Blog post:https://huggingface.co/blog/the_n_implementation_details_of_rlhf_with_ppo 💾 Code: https://github.com/vwxyzjn/lm-human-preference-details submitted by /u/vwxyzjn [link] [comments]  ( 9 min )

What are best ways to collect database for any ml project submitted by /u/GingSkywalker [link] [comments]  ( 8 min )

We are excited to announce the publication of our groundbreaking scientific paper in Machine Learning: Science and Technology titled “Feature Space Reduction Method for Ultrahigh-Dimensional, Multiclass Data: Random Forest-Based Multiround Screening (RFMS)” by Gergely Hanczar, Marcell Stippinger, David Hanak, Marcell T Kurbucz, Oliver M Torteli, Agnes Chripko, and Zoltan Somogyvari. Published on: 19 October 2023 DOI: 10.1088/2632-2153/ad020e Volume 4, Number 4 In recent years, several screening methods have been published for ultrahigh-dimensional data that contain hundreds of thousands of features, many of which are irrelevant or redundant. However, most of these methods cannot handle data with thousands of classes. Prediction models built to authenticate users based on multichannel biometric data result in this type of problem. In this study, we present a novel method known as random forest-based multiround screening (RFMS) that can be effectively applied under such circumstances. The proposed algorithm divides the feature space into small subsets and executes a series of partial model builds. These partial models are used to implement tournament-based sorting and the selection of features based on their importance. This algorithm successfully filters irrelevant features and discovers binary and higher-order feature interactions. To benchmark RFMS, a synthetic biometric feature space generator known as BiometricBlender is employed. Based on the results, the RFMS is on par with industry-standard feature screening methods while possessing many advantages. r/IAMA - Oct 26 with the founders of Cursor Insight. https://bit.ly/AMAwithCursorInsight-GoogleCalendar ​ R/IAMA - Oct 26 with the founders of Cursor Insight. submitted by /u/CursorInsight [link] [comments]  ( 9 min )

Signatories include Turing Award winners Yoshua Bengio, Geoffrey Hinton, as well as others academics and experts. In 2019, GPT-2 could not reliably count to ten. Only four years later, deep learning systems can write software, generate photorealistic scenes on demand, advise on intellectual topics, and combine language and image processing to steer robots. As AI developers scale these systems, unforeseen abilities and behaviors emerge spontaneously without explicit programming1. Progress in AI has been swift and, to many, surprising. The pace of progress may surprise us again. Current deep learning systems still lack important capabilities and we do not know how long it will take to develop them. However, companies are engaged in a race to create generalist AI systems that match or ex…  ( 10 min )

Hey guys, I sometimes post about tiny ML projects we work on. This time, we talk about applying language models for generating recipe titles/ideas. Specifically, we don't use LLMs, and this turns out to be a bit of a controversial decision, but one that has it's own advantages. Quite interested in the community's take on it: https://engineering.hellofresh.com/recipes-and-generative-ai-6d74a107860c submitted by /u/abnormdist [link] [comments]  ( 9 min )

📷Research https://arxiv.org/abs/2310.08754 While the recent success of LLMs has been driven primarily by curation of training dataset composition, scaling of model architectures and dataset sizes, and advances in pretraining features, the impact of tokenizers has often lagged as a blind spot. Our researcher*s study sheds light on this issue and shows that tokenizer choice can significantly impact downstream model performance as well as training and inference costs. 1️⃣ Investigation of intrinsic tokenizer performance, i.e., study of tokenizer properties (i.e., generated vocabulary), and tokenization results of tokenizers. 2️⃣ Investigate the extrinsic performance of the tokenizer, i.e., the impact of the tokenizer on the downstream performance of the model. 3️⃣ Investigation of possible correlation between intrinsic and extrinsic tokenizer performance. ​ 💡 The investigation shows that the common tokenizer evaluation metrics "fertility" and "parity" do not always predict the performance of the downstream model, making these metrics a questionable criterion for tokenizer evaluation. 💡 Moreover, the study shows that multilingual tokenizers - which are based on the five most common European languages - require a vocabulary size by a factor of three compared to English. The previous approach of training tokenizers with English vocabulary only thus turns out to be inefficient and results in a strong performance degradation and additional training costs of up to 68% submitted by /u/effi28_ml [link] [comments]  ( 9 min )

I'd love to hear your guys thoughts on next steps to improve this, maybe deeper layers and more nodes, maybe a random forest is more appropriate? I'd love to hear any thoughts on Machine Learning directly applicable to time-series data. https://www.quantitativefinancialadvisory.com/post/asset-allocation-in-a-post-modern-portfolio-theory-world-part-1-the-single-layer-taarp-ml-model The Main Idea We will develop a Machine Learning model, specifically a deep learning model (more hidden layers to come), to periodically, tactically rebalance the weights of our portfolio based on observable market data and empirically determined statistics combined with feature engineering from the past 21 trading days, and for the VIX we consider its characteristics since inception. The output will be a range representing the degree to which we bet long, short, or hold cash, and 3 weights that sum to less than or equal to one and greater than or equal to negative one. In essence we will allow shorting of securities and not require our portfolio to be fully invested. Cash is an active position; sometimes the best investment is staying on the sidelines. The model will allow one input layer, one and two hidden layers (to show that more might not always be better, explicitly with the 200 variable maximum excel solver imposes on us), and an output layer with 3 nodes outputting a value between -1 and +1 with -1 representing a full allocation to a short position in the security and +1 representing a fully allocated long position. submitted by /u/QFA_official [link] [comments]  ( 9 min )

As an outsider who has many friends in ML Phds, this is my perspective of their lives: long hours, working nights, weekends no work-life balance, constant fear of being scooped and time pressure from deadlines frustrating broken review systems many incremental, advertisement papers that produce very little actual contribution (which is justified by 2.) "engineering" and not "science" all this pressure amounts to severe imposter syndrome Are people in the field still happy? Where do people get their satisfaction? To me it looks like almost like a religion or a cult. The select few who say, get neurips outstanding paper are promoted to stardom - almost a celebrity status while everyone else suffers a punishing work cycle. Are the phd students all banking on AGI? What else motivates them? Edit: the discussion is about whether 1-6 are worse in ML than other fields (or even the median experience). The reference for "other field" is highly heterogenous. Experience obviously varies by lab, and then even by individuals within labs. "It happens in other fields too" is a trivial statement - of course some version of 1-6 affects somebody in another field. Edit 2: small n but summarizing the comments - experience seems to differ based on geographic region, one's expectations for the phd, ability to exert work-life balance, and to some extent ignore the trends others are all following. Some people have resonated with problems 1-6, yet others have presented their own, anecdotal solutions. I recommend reading comments from those who claim to have solutions. submitted by /u/shenkev [link] [comments]  ( 9 min )

➡️ Check on https://huggingface.co/spaces/xuyingliKepler/VecDBCompare 📌 Introduction: VecDBCompare is a streamlit-based application designed to evaluate and compare three different vector database retrieval strategies. Users only need to upload a PDF and interact with QABots using three different strategies to determine which strategy is most suitable for them. ⭐️ Three retrieval strategies: Chunk Strategy: Divides the document into small chunks and retrieves based on the most relevant chunks. Summary Strategy: Summarizes the document and retrieves based on the summary content. Hypothetical Question Strategy: Generates hypothetical questions that the document might answer and retrieves based on these questions. submitted by /u/xuying_li [link] [comments]

I have ~1 million 3D design (.STP and/or .OBJ) files of various parts for medical devices, aerospace, automotive or defense systems. I'd like to label them based on appropriate manufacturing methods that are used to physically make them. Some example methods and labels would be milling, turning, injection molding, cnc machining, etc. After labelling, I'd like to architect a system to produce these labels as inference for a new part that has not been physically made yet. My team (<5 people) have manufacturing domain expertise and can manually label these parts but I'm looking for a more scalable solution that isn't as time consuming. Crowd sourced methods like Mechanical Turk won't work because annotators do not have the domain knowledge to mark the correct label. Labelling platforms like SageMaker/Azure ML Studio only allow image/text/audio datasets, is there a platform that'll help me setup labelling tasks for 3D designs? Furthermore, how can I find more experts that can help scale this up? It seems to me that the only option is to build my own labelling app as an annotator needs these key features - 3D model visualizer so they can spin the part and view any orientation Draw a bounding box (commonly available in other platforms) Toggle measurements in inches/mm As for label classification I'm looking at architectures like PointNet since my dataset of meshes can be sampled to point clouds. Are there other methods that would work better or worth exploring? Open to any and all suggestions across this pipeline. ​ ​ submitted by /u/rootcage [link] [comments]  ( 9 min )

I’m an undergraduate who’s considering a PhD student in ML. I’m currently in a lab that focuses on ethics in AI. While I love the work, it focuses on the humanities side of CS. I’ve always been a more mathy person and have always been interested in theoretical ML research. I’d like to combine ethics & AI/ML in some way (eg studying explainable AI from the technical perspective). I was wondering what are some research areas that combine the two and if I don’t work in academia, what’s the market and job prospects like for someone who does this? submitted by /u/SnooChipmunks1902 [link] [comments]  ( 9 min )

Hello all, I'm working on Montezuma's Revenge using the Gymnasium API. I wonder if there's anyone here that knows the numerical value of the rewards? And if so, how they are typically scaled down. ​ Thanks! ​ G_bes submitted by /u/G_bes [link] [comments]

submitted by /u/vwxyzjn [link] [comments]

Hello, I would like to create my own environment (Maze), in which I would like to train my drone using reinforcement learning, I am kind of new and I don't know how can I set the state space, rewards, and if I would like to use BS3 for training then how can I connect the environment? And for the agent which is the drone, should i just do the AirSim build.cmd and take the agent from there and place the starting position flag or what? I am a bit lost and I can't find tutorials on how to do this, I'd appreciate it if you could provide some guidance. Thanks in advance. submitted by /u/Gabii99 [link] [comments]

In today’s information age, the vast volumes of data housed in countless documents present both a challenge and an opportunity for businesses. Traditional document processing methods often fall short in efficiency and accuracy, leaving room for innovation, cost-efficiency, and optimizations. Document processing has witnessed significant advancements with the advent of Intelligent Document Processing (IDP). With […]  ( 20 min )

This post is co-authored by Dhurjati Brahma, Senior Systems Architect at T-Mobile US, Inc and Jim Chao, Principal Engineer/Architect at T-Mobile US, Inc and Nicholas Zellerhoff Associate Systems Architect at T-Mobile US, Inc. T-Mobile US, Inc. provides a Voicemail to Text service to its customers, which allows customers to quickly read through their voicemails and […]  ( 7 min )

Announcements Top Stories In-Depth The post DSC Weekly 24 October 2023 appeared first on Data Science Central.  ( 21 min )

Written by Venkata Nori and Kshitij Gopali. Introduction As technology is evolving, most companies in the world are adopting advanced mechanisms for their daily tasks of storing/updating data, project management & tracking, incident management, version control, etc. Periodically, these companies’ business stakeholders would want to extract and analyze the data to see how the business… Read More »Seamless integration of data from unconventional source systems into Business Intelligence using data science techniques The post Seamless integration of data from unconventional source systems into Business Intelligence using data science techniques appeared first on Data Science Central.  ( 25 min )

A recent interview by Medical Device Network with GlobalData medical analyst Alexandra Murdoch shares interesting insights into cybersecurity for medical devices. The post How data science and medical device cybersecurity cross paths to protect patients and enhance healthcare appeared first on Data Science Central.  ( 22 min )

In the contemporary business landscape, where data is heralded as the new oil, Business Analytics has emerged as a pivotal domain, steering organizations towards informed decision-making and strategic planning. business analytics encompasses the utilization of data, statistical algorithms, and machine learning techniques to comprehend the business context, forecast future trends, and facilitate optimal decision-making. The… Read More »Skills required to excel in a business analytics career The post Skills required to excel in a business analytics career appeared first on Data Science Central.  ( 22 min )

In an era where data drives decisions, GenAI emerges as a prodigy force in the realm of data analytics. According to Statista, LLM’s market size is expected to show an annual growth rate of 24%, resulting in a market volume of $207 bn by the end of 2030.  This cutting-edge technology, built on sophisticated algorithms… Read More »GenAI: The game-changer in data analytics The post GenAI: The game-changer in data analytics appeared first on Data Science Central.  ( 22 min )

submitted by /u/nickb [link] [comments]

A couple days ago I wrote about the likelihood of the better team winning a best-of-five or best-of-seven series. That is, if the probability of X winning a game against Y is p > ½, how likely is it that X will win a majority of 5 games or a majority of 7 games. This […] Best of N series first appeared on John D. Cook.  ( 6 min )

I discovered the Space Rocket History Podcast a while back and listened to all the episodes on the Apollo program. I’m now listening to the episodes on Skylab as they come out. I came for Apollo; I stayed for Skylab. I would not have sought out the episodes on Skylab, and that would have been […] Lessons from Skylab first appeared on John D. Cook.  ( 6 min )

This post will compute the center of curvature for an object described in the previous post. In order to do that we first need to describe principle curvature and Gauss curvature, and we’ll throw in mean curvature while we’re at it. Let S be a surface sitting in three dimensional space. No need for more […] Curvature: principal, Gauss, and mean first appeared on John D. Cook.  ( 6 min )

One year ago I wrote about a variant of the squircle that is quantitatively close to the customary definition but that has nicer algebraic properties. That post used the term p-squircle for the usual squircle with equation where p > 2, and the term s-squircle for the variation with equation where s is between 0 […] An algebraic superegg first appeared on John D. Cook.  ( 5 min )

Visual effects artist Surfaced Studio returns to 'In the NVIDIA Studio' to share his real-world VFX project, created on a brand new Razer Blade 16 Mercury Edition laptop powered by GeForce RTX 4080 graphics.  ( 8 min )

Hi community, first time posting here. I'm working on a project for the segmentation and classification of traffic signs using eCognition Developer software. I need help with creating scripts to apply three classifiers: Naive Bayes, SVM, and Random Forest. I'd like to know how I can implement these classifiers in eCognition Developer and where to insert the scripts in the software. Does anyone have experience with this software and could share script examples or provide guidance on how to accomplish this task? Sorry English is not my first language. Tldr, i need to include the Bayes classifiers, Random Tree, and SVM in eCognition Developer (for segmentation and classification - prediction). submitted by /u/Dignai [link] [comments]  ( 9 min )

gpt4docstrings is a Python library that allows you to write docstrings for functions / classes non documented in your codebase. In this case, I'm applying the library to one module of langchain to see the results. Repo: https://github.com/MichaelisTrofficus/gpt4docstrings https://i.redd.it/78f3wit071wb1.gif submitted by /u/Hefty-Consequence443 [link] [comments]  ( 9 min )

Heey everyone! I hope you're doing well. I need your help guys. I'm working on a DQN that outputs a binary vector of length L (I just applied sigmoid function on the ouptut layer and take p>0.5 as 1 and 0 otherwise). In this setting, at each decision time, the agent returns a list containing the indices of selected elements. Knowing that the list's length is dynamic how can I train my DQN ? (I am facing issues in this). Is there any alternative way to do this purpose (like DDPG :/ )? submitted by /u/GuavaAgreeable208 [link] [comments]  ( 9 min )

Hi, first of all, sorry for the cross post, but I guess Huggingface forums were not the right place to begin with and it took me a while to find out where things about AI/ML are being actively discussed. I am a professional software developer (C, Python on Linux) and while I did try out a few things with PyTorch and Diffusers - I am not an ML engineer, so I am looking for someone with ML expertise who’d be interested to team up for a non commercial open source project. I can do quite a lot around application development, but I clearly lack the required ML knowledge. I followed the free MIT ML courses on YouTube, did some reading, tried things out, but the ML part of this project is for sure over my head. So, here’s what I have in mind: I would like to create an application which would b…  ( 11 min )

Hello, We are running a number of machine learning models in production and would like to monitor some metrics during inference: Data quality, inference time, accuracy, etc. All these metrics could be recorded in the python code and we are planning to build a SQL database that will receive all the information so as we can visualize in grafana. Do you think this is a good pattern? What would you suggest instead (we are using AWS). Thank you in advance. submitted by /u/Eddas123 [link] [comments]  ( 9 min )

A while back, I came across the "From Variational to Deterministic Autoencoders", which provided a novel insight into the generative properties of autoencoders by framing the objective through the lens of regularization. However, I couldn't help but notice that the deterministic models studied felt incomplete, namely due to the inherent lack of sampling in those models (which is something that the authors acknowledge). To provide a short recap of the paper, the authors surgically decompose the variational autoencoder objective into a deterministic one. They start with a Constant-Variance VAE, which is a special case of the general Gaussian latent VAE where the noise standard deviation of the latent distribution is fixed to 1. This leads to what is essentially a standard autoencoder with t…  ( 10 min )

Example: Suppose a character-level language model (three input letters to predict the next one), trained on a dataset that contains three instances of the sequence aei, with two occurrences preceding o and one preceding u, i.e., the dataset is: Input Output aei o aei u aei o In this case, the ideal probability distribution for the model's logits for aei would be ~0.66 for o, ~0.33 for u, and zero for other letters. In other words, when the model is input with aei, the ideal softmax of the logits would be ~0.66 for o, ~0.33 for u, and zero for other letters. Following this reasoning, the objective is to optimize the model's output for a given input to match the distribution of occurrences in the dataset. If this reasoning is correct, then we have the following ideal loss (cross-entropy): https://preview.redd.it/pzpxogcqd0wb1.png?width=330&format=png&auto=webp&s=b0b6c3b5fbfb4797c11a1f26375065ce883551d3 Thus, ~0.63 is the smallest loss we can get with this dataset. Is my reasoning correct? submitted by /u/viniciusarruda [link] [comments]  ( 9 min )

Basically im working on the DDPG algorithm in DRL where i have an actor and critic networks. The actor network architecture is quite simple: Input layer contains 22 neurons that represents the state values (ranging from 0.1 to 10.0 max not normalizing them) Two hidden layers with 128 neurons, with Leaky Relu activation (alpha = 0.01), and with HeUniform kernel initialzer Output layer with a single neuron has tanh activation, using Glorot kernel initialzer The critic network has the same architecture but we only concatenate the 22 state values with the action produced by the actor, the only difference is the ouput of the critic has no activation. And both networks use Adam. The problem arises when the training starts because i run a few steps without actually start the learning, but when the learning starts, the actor converges quickly to output values 1 or -1 afor any given input. I tried many learning rates for both actor and critic. One thing to note is when i set the actor learning rate to 1e-5 and the critic to 1e-3 the networks sometimes converges quickly, some time it takes longer to converge and sometimes it does not converge. submitted by /u/Desert_champion [link] [comments]

I have been looking for a pre-trained VAE (on Imagenet with ResNet/VGG) or similar which I could fine-tune on my smaller dataset. However, not only there does not exist many such pre-trained weights but the practice of fine-tuning VAEs does not really seem mainstream. Is there a reason why VAEs are not pre-trained/fine-tuned? Does it have to do with posterior collapse? submitted by /u/unholy_sanchit [link] [comments]  ( 9 min )

There is an architecture for images/videos called MViT, where 2D MaxPooling layers are added to reduce computations for ViT. But MaxPooling has a drawback - it discards information independently of context, equally discarding information from both important and uninformative parts of the image. For traditional Conv2D networks, there's little we can do about this, but for transformers, we can reduce dimensionality in a more meaningful way - discarding only those elements that don't carry unique information. Are there any articles/developments on this topic already? submitted by /u/Dependent_Bluejay_45 [link] [comments]  ( 9 min )

Hello, I'm currently using RVC AI, and I'm about to record myself for the training. What is the best way to record myself except the singing and talking at least for 15 minutes like the guide says. Do I have to make it 20 min and one audio file or do I have to make it 20 min and maybe 10 files with 2 minutes each file? Also, can I multiply my files and reach the 15-20 minutes of audio that it's required or I have to make a different talking or singing for every audio? submitted by /u/WeldFrenzy [link] [comments]  ( 9 min )

Still searching for a model that is well enough to make RAG... Lots of good models on huggingface, but none of them is trained to return extracted text or answers based on provided info without hallucinating something. Is quite frustrating, every week came a new version of a model that is amazing for Role play and storytelling... (some good progress also on coding...) I see lots of efforts in different RAG strategy, improving semantic search and Chunking, but the open source community still does not have a decent model fine tuned for that. I have considered the idea of make that fine tune, based on synthetic data (using Wikipedia as knowledge base), but unfortunately I have not enough funds to cover the api cost neighter to pay for some decent Gpu. I'm not going to train a 7B Model because the under 30B imho doesn't have many sense if the coherence is the main requirements. Unpopular opinion: as coherence, code llama 34B is much better to any of the 70B fine tune. Sorry to everyone for the rant... Does anyone have some tips or suggestions? Thanks in advance! Edit: My database is composed mainly by abstracts of papers and medical textbook. I admit that the domain is quite complex, but the error rate is too high. Obviously that even if prompted to avoid that (tried and refined multiple prompts, using different prompt format). Gpt3.5, Claude instant and Palm2-Bizon work fine for that task. (obviously GPT4 and Claude 2 would be best, but too expensive for me) I spent lots of time to make a solid embedding pipeline: advanced chunking, Metadata added by llm, text for similarity search different from text provided to LLM, instructor bi encoder to generate embeddings(INSTRUCTOR-XL), reranking using cross encoder, RAG-Fusion using multiple query and HyDE approach Hybrid search with BM25 So... I'm a bit frustrated that i can not run all locally, became that is a must for my project. submitted by /u/Distinct-Target7503 [link] [comments]  ( 10 min )

https://arxiv.org/abs/2310.13017# Linear position interpolation helps pre-trained models using rotary position embeddings (RoPE) to extrapolate to longer sequence lengths. We propose using linear position interpolation to extend the extrapolation range of models using Attention with Linear Biases (ALiBi). We find position interpolation significantly improves extrapolation capability on upstream language modelling and downstream summarization and retrieval tasks. submitted by /u/jwan584 [link] [comments]  ( 9 min )

As context, I'm personally working on a project to make ML/AI research publication more reproducible. We're backed by Balaji Srinivasan (https://twitter.com/balajis) at the level of funding and advice. It seems like, despite attempts like Jupyter Notebooks or sites like Papers with Code, most published research in ML still isn't setup to be easily reproducible. Even companies like Anthropic/OpenAI don't put much of an emphasis on reproducibility, even though it's in their interest to do so to earn public trust. Our current hypothesis is to conceptualize reproducible research as software testing. Specifically we're thinking of building tools that let you internally test the robustness of results, and externally publish them s.t. they're reproducible. You can think of it as continuous integration for reproducible research; e.g. BuildBot for Reproducible Research. One specific idea I have is to build a model evaluation/testing platform that lets you: Internally eval LLM models on open benchmarks (TruthfulQA, AGIEval, etc.) Test robustness of results under different assumptions Externally publish reproducible results I don't have a background in ML research. So I'm looking to get input from research engineers on what challenges/barriers currently exist with model testing and publishing reproducibly — so I thought I'd reach out in this community if anyone's open to that! Let me know if this post doesn't conform to the rules, or if this should go somewhere else. submitted by /u/manveerbasra [link] [comments]  ( 9 min )

Hello everyone, I am currently trying to find suitable image captioning and visual question answering models to implement in my project. After a quick google search I came across BLIP2 from hugging face however, its a very large model overall and both my pc and colab could never load its lightest pretrained version. Does anyone know any similar pretrained models for the specific tasks or any other way to load this kind of large model? (I tried loading it with 8bit precision which still failed) I have 16gb of RAM and the task requires image captioning and the ability to ask the model details about the specific image. Any help is greatly appreciated!! submitted by /u/Spitefulsalamander [link] [comments]  ( 9 min )

I'm new to few-shot learning and I'm having trouble understanding why prototypical networks use a random sub-sampling approach while the vanilla few-shot learning approach uses episodic training. Doesn't random sub-sampling fail to guarantee that data overlapping won't occur? submitted by /u/The_Aoki_Taki [link] [comments]  ( 9 min )

I implemented a label propagation algorithm which is mainly used in the field of Video Object Segmentation (VOS). Basically I provide the labels for one frame and ask my model (using pre-trained encodings of frames) to do semantic segmentation on all the other frames of a video. I am obtaining consistently better results using an high temperature softmax when computing the similarity between pixels of different frames. Then the top-k similarities of each pixel (features) are used to propagate the labels from one frame to the next. I will not disclose the dataset I am using but let's say it is noisy (let's say also low quality). I want to understand why an high-temperature softmax performs better than a softmax with T=1 or an extreme T = 0.01. At the moment I get better results with T = 10, 100 and the trend in my grid search shows that even higher T could be possible. I was wondering if the model is still considerable valid if T is too high. I feel like the model is almost randomly guessing, if T is too high, but this apparently enhances performance. Every help is appreciated. Also literature about the topic! I only found one paper (which uses an high-temperature softmax to distill knowledge in a student-teacher network for remote sensing imagery) submitted by /u/darthjeio [link] [comments]  ( 9 min )

Hi everyone, I have some old code written in tf1. It has not been ported to tf2 or pytorch yet. Does anyone of you have leads on whether one can implement custom callback for tf1 code and if there are any examples on the web? Thanks in advance. submitted by /u/wrik003 [link] [comments]  ( 9 min )

In the tech report of GPT4, an analysis was conducted on the impact of different languages on model performance. These effects are attributed to the amount of data and language characteristics. This also indicates that the model's effectiveness may not meet the expectations of users in different languages. The problem addressed in this paper is of significant importance. https://preview.redd.it/s48419fe9yvb1.jpg?width=2748&format=pjpg&auto=webp&s=ba76f1bd18043c6cb2610ed90f5c41a78b5ccd95 Arxiv: https://arxiv.org/abs/2310.13683v1 Stay updated with AI in a fun-to-listen way. Check out ai-dailynews.com to generate your personalized news podcast🎙. It's one of my open-source projects and takes no charge. submitted by /u/xuying_li [link] [comments]  ( 9 min )

The paper employs a deep learning system to learn from the great composer Beethoven and capture his composition ability in a hash-based knowledge base. This new form of knowledge base provides a reasoning facility to drive the music composition through a novel music generation method. https://preview.redd.it/l9gzcoe38yvb1.png?width=1944&format=png&auto=webp&s=d6c5ca7f8fe434be1187c1f0440c5a94ebfc9b64 Arxiv: https://arxiv.org/abs/2310.13691v1 For more AI updates, check out this AI-generated news podcast🎙 tailored to your preferences(ai-dailynews.com), which is open source and free. submitted by /u/xuying_li [link] [comments]  ( 9 min )

The biclustering algorithm partitions rows and columns of a matrix into clusters so that the variance inside each intersection between row and column clusters in minimized. I want to perform the biclustering of a matrix, but additionally to enforce that the row and column clusters are the same, i.e. if the row i lies inside a row-cluster c then the column i must lie in a column-cluster c. Rows and columns in the matrix represent the same entities (but the matrix is non-simmetric). sklearn implementation does not support such a constraint. Are there any algorithms for this at all? submitted by /u/Tomarchelone [link] [comments]  ( 9 min )

Hey all, I'm building this open source project that helps ML engineers evaluate LLM applications (its like unit testing for LLMs), and it works great in development since users can just write a test_file.py like how you would normally do it in pytest, but as I'm going onto the next phase I'm thinking how to bring evaluation to production, especially on metrics such as factual consistency where I need a ground truth. I'm hoping to get some ideas around this. Here's a link to the repo (https://github.com/confident-ai/deepeval) if you want more clarity on what the package looks like, but most importantly any help to brainstorm production evaluation will be greatly appreciated. Thank you very very much! submitted by /u/Ok_Constant_9886 [link] [comments]  ( 9 min )

In ICCV23, several top notch researchers shared their insights (in a workshop called “Quo Vadis, Computer Vision?”) wrt the current state of Computer Vision, especially in light of the meteoric raise of LLMs. Has CV stalled? Is CV dead? E.g.MIT’s professor Bill Freeman, has some interesting points on foundation models: “FM aren’t fundamental, therefore not stable". Jitendra Malik argues "video can describe the world better than text." submitted by /u/btcmx [link] [comments]  ( 9 min )

Hey everyone! I am looking for papers that propose or explore biologically plausible vision models, primarily tasks like classification and grasping (predicting grasping bounding boxes) tasks. By biologically plausible, I mean papers that propose models inspired by the human brain in some way or the other. I know convolution is loosely inspired by human cognition, but everything I can find seems to suggest the opposite for ViT like models. I have come across certain papers like these: - https://arxiv.org/abs/1901.00945 - https://proceedings.neurips.cc/paper/2020/hash/98b17f068d5d9b7668e19fb8ae470841-Abstract.html But I am still looking for more. Any suggestions? submitted by /u/Far_Clothes_5054 [link] [comments]  ( 9 min )

I was trying to comprehend the math behind this paper: https://arxiv.org/pdf/2006.11239.pdf. You can see in the equation corresponding to the forward diffusion process, at each time step, the image in the previous step is also scaled by sqrt(1-beta_t) while adding noise. It seems like the purpose of this is to maintain a fixed variance (or specifically, unit variance) at each time step. My question is: What is the significance of maintaining unit variance at each time step? Why is this useful? I saw somewhere that this is done to prevent the variance from "exploding." I don't really know what this means. I guess the variance keeps on increasing if the scaling isn't done. But why is this bad? submitted by /u/fallendeviL701b [link] [comments]  ( 9 min )

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I'm not sure how to enable BERT with flash attention during the start-up of the Triton server in order to accelerate inference. Dao(the author of FA) told me he’s never tried. submitted by /u/g14loops [link] [comments]  ( 9 min )

If you are an Etsy seller or know someone who sells on Etsy, or maybe you went on Etsy and your favorite store is gone, could be due to the Etsy bots taking down stores for not figuring out properly which Mockup Images are real and which ones are AI Generated. All you have to do to find this out is go on youtube or social media and look for "etsy mockups news". Also Etsy has been pretty quiet about this and as a result Etsy sellers are going crazy about this as no one knows why some stores who haven't used AI to create their mockups are being targeted by these bots. This just goes to show how hard is getting to distinguish between what is real and what is AI generated and how across all industries companies are having issues adapting to AI technology changes. Thoughts? submitted by /u/fk1220 [link] [comments]

Nightshade is a new data poisoning tool that allows artists to fight back against generative AI models. By adding invisible changes to the pixels in their art, artists can cause chaos and unpredictable results in AI models that use their work without permission. The tool, called Nightshade, is intended as a way to fight back against AI companies that use artists’ work to train their models without the creator’s permission. Using it to “poison” this training data could damage future iterations of image-generating AI models, such as DALL-E, Midjourney, and Stable Diffusion, by rendering some of their outputs useless—dogs become cats, cars become cows, and so forth. AI companies such as OpenAI, Meta, Google, and Stability AI are facing a slew of lawsuits from artists who claim that th…

ChatGPT and Bard are cool, but I have to manually feed them transcripts generated by Whisper to get summaries. Furthermore, since the length of the transcript is often longer than the maximum character limit(s), I have to add additional prompts in between copying and pasting multipart transcripts. Since these recordings are 10–15 years old, the audio quality isn't the best, but I think it's sufficient to generate transcripts + detect speech, if not, I might need an additional "audio cleaning" step as well. I don't mind paying, and I'm above average in technical ability, so if anyone has any suggestions, I'd love to hear them. Here's what the workflow would look like: INPUT: I will upload a folder containing 100+ MP3 files of podcasts with below-average audio quality. OUTPUT: I would like to get a Google Doc or a Text file with 1-page summaries of the most important points in bullet-point format corresponding to each episode. Each page should be separated by some sort of divider, and the header should contain the filename for reference. Ideally, there should be an existing Jupyter Notebook I could throw in Google Colab and do all of the above in a plug-and-play manner, but if not, I'd love to hear your thoughts. Any tips? Thanks! submitted by /u/aknalid [link] [comments]

https://www.technologyreview.com/2023/10/16/1081149/ai-consciousness-conundrum/ "AI consciousness isn’t just a devilishly tricky intellectual puzzle; it’s a morally weighty problem with potentially dire consequences. Fail to identify a conscious AI, and you might unintentionally subjugate, or even torture, a being whose interests ought to matter. Mistake an unconscious AI for a conscious one, and you risk compromising human safety and happiness for the sake of an unthinking, unfeeling hunk of silicon and code. Both mistakes are easy to make." "Every expert has a preferred theory of consciousness, but none treats it as ideology—all of them are eternally alert to the possibility that they have backed the wrong horse." "The trouble with consciousness-­by-committee, though, is that this state of affairs won’t last. According to the authors of the white paper, there are no major technological hurdles in the way of building AI systems that score highly on their consciousness report card. Soon enough, we’ll be dealing with a question straight out of science fiction: What should one do with a potentially conscious machine?" "For his part, Schwitzgebel would rather we steer far clear of the gray zone entirely. But given the magnitude of the uncertainties involved, he admits that this hope is likely unrealistic—especially if conscious AI ends up being profitable. And once we’re in the gray zone—once we need to take seriously the interests of debatably conscious beings—we’ll be navigating even more difficult terrain, contending with moral problems of unprecedented complexity without a clear road map for how to solve them." submitted by /u/kamari2038 [link] [comments]

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A new AI agent Eureka developed by NVIDIA Research that can teach robots complex skills has trained a robotic hand to perform rapid pen-spinning tricks — for the first time as well as a human can.[1] Meta’s Habitat 3.0 simulates real-world environments for intelligent AI robot training.[2] South Korea’s SK telecom Co. will collaborate with Deutsche Telekom AG to jointly develop a telecommunications-specific artificial intelligence (AI) large language model (LLM) as competition intensifies among local telecom companies to expand overseas with their own AI capabilities.[3] Scientists say they have built an artificial intelligence (AI) tool that can successfully identify and confirm supernovas.[4] Sources: [1] https://blogs.nvidia.com/blog/2023/10/20/eureka-robotics-research/ [2] https://siliconangle.com/2023/10/20/metas-habitat-3-0-simulates-real-world-environments-intelligent-ai-robot-training/ [3] https://pulsenews.co.kr/view.php?year=2023&no=810112 [4] https://learningenglish.voanews.com/a/researchers-build-first-tool-to-discover-supernovas/7318435.html submitted by /u/Excellent-Target-847 [link] [comments]

I've been using ai for a long time, it often helps me to reduce my work time, but I want to try to earn money and decided to make an investigation. I want to hear your opinion on my analysis, and maybe this post will help someone in starting a business through ai Joe Popelas, a very young entrepreneur, has made over a million dollars within the last year selling AI-generated books online. I literally got fascinated by how simple yet powerful it is with these tools to create a book within a matter of a few hours. Joe Popelas is one of a new breed of AI entrepreneurs who capitalized on the democratization of large language models. Joe's story demonstrates the power of combining human creativity with AI. While AI tools did the heavy lifting for his initial drafts, Joe spent time refining …

This post is co-authored by Anatoly Khomenko, Machine Learning Engineer, and Abdenour Bezzouh, Chief Technology Officer at Talent.com. Founded in 2011, Talent.com is one of the world’s largest sources of employment. The company combines paid job listings from their clients with public job listings into a single searchable platform. With over 30 million jobs listed […]  ( 12 min )

Images such as those in Google Street View are taking on a new purpose in the hands of University of Florida Assistant Professor of Artificial Intelligence Chaofeng Wang. He’s using them, along with deep learning, in a research project to automate the evaluation of urban buildings. The project aims to help governments mitigate natural disaster Read article >  ( 6 min )

Hello, everyone! I'm building a MARL agent for a zero-sum game and I'm having a hard time evaluating it. I managed to quickly train it for a simple case and I could manually verify that it was actually learning the optimal decision making because I already know how the game works and, for this simple case, I know that there actually is a mathematically correct way to play it (from both sides) and how it should be played, but that isn't true for most cases (and even if it was, I wouldn't be able to manually verify thousands of games). To complicate things even more, there are billions and billions of possible initial states. For single-agent RL, I could set a reward threshold (if I knew which was the maximum reward possible) or at least I could set a maximum time of "no improvement" but, in a zero-sum game, the sum of the policy rewards is, well, zero. I could think of two solutions: Evaluate convergence to Nash Equilibrium on a subset of the possible initial states, which could be a problem because I'm not sure if the game dynamics guarantee the existance of Nash Equilibria; Evaluate convergence of the winrate of the trained agent against a "hand-crafted" baseline agent, which could be a problem because the quality of this evaluation method could depend on how well I can make this baseline agent (which won't be even close to optimal, otherwise I wouldn't be training an agent). Any thoughts? submitted by /u/victorsevero [link] [comments]

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Didn't see anything about this kind of post in the rules I'm asking for a tattoo idea haha submitted by /u/victorsevero [link] [comments]

I am making a thesis about using RL to solve the inverted pendulum swing-up problem. I have tried using TD3, SAC, and TD3-Fork. In my testing, TD3-Fork worked best, I think SAC would also work if I am able to tune the hyperparameters correctly. I would like a similar trained agent to td3 converged where the agent balances the pole almost indefinitely. I have tried the hyperparameters from the website and also different hyperparameters but it has not converged. I am wondering if I am missing something or if there is anything I can do to improve the agent. I have been thinking of using HER instead of FORK. Any help or advice would be appreciated. training reward data The 'maximum' reward that I could get in the simulation is >880. The reward function that I used is -[cos(theta) + 10(|x| > 0.9) + 10(|theta_dt| > 18)]. However, from the data above it only converges to about 837 max and rarely reaches >900. trained td3 fork agent submitted by /u/YEEETTT0708 [link] [comments]

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The 15th Kendall Square Association annual meeting explored new and old aspects of the neighborhood.  ( 9 min )

What is nonlinear algebra? Negations are tricky. They may be the largest source of bugs in database queries. You have to carefully think about what exactly are you negating. Any time you see “non-” attached to something, you have to ask what the context is in which the negation takes place. For example, if you […] Nonlinear algebra first appeared on John D. Cook.  ( 6 min )

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Today on “Abstracts,” Partner Research Manager Andy Gordon & Senior Researcher Carina Negreanu explore new work introducing co-audit, a term for any tool-assisted experience that helps users of generative AI find and fix mistakes in AI output. The post Abstracts: October 23, 2023 appeared first on Microsoft Research.  ( 16 min )

Hi r/MachineLearning, My name is William and I’m one of the founders of Sweep. Sweep is an AI junior developer that writes and fixes code by mirroring how a developer works. While building Sweep, we used to use the Github API, but we ran into rate limits, so we changed this to clone your repository for the duration of the request. It's now coming full circle. Sweep can now write, run, and debug a failing unit test for the ClonedRepo class! Blog: https://docs.sweep.dev/blogs/ai-unit-tests Video: https://www.youtube.com/watch?v=N9PUxmja9z4 submitted by /u/williamsweep [link] [comments]  ( 9 min )

So essentially what the title says. I want to truly understand whats happening behind Machine Learning in general and also behind each algorithm specifically (starting from the basics to more advanced things, like Logistic Regression, Decisions trees and random forests, Deep Learning, NLP, GANS...). By structured I mean it contains all the pieces ordered and organized, from the same source, so you can can actually go from the building blocks up, not just a YouTune channel that uploads interesting videos about different machine learning related topics. Regarding the medium, I don't really mind but I would prefer audiovisual content (YT channel/playlists, Lectures, conferences...) but if you really recommend a specific book or series of books that's also okay. If it has some practical focus to it (to better grasp the theory) that would great. Also, I would prefer if it goes deep into the details, but not too deep into the specific maths involved, but if it's the case thats also okay. Regarding price, obviously if it's free that would be awesome, but in the range of free to 40€ is fine. Thank you for your recommendations in advance!! submitted by /u/aleradamantis [link] [comments]  ( 9 min )

Guys does anyone have an idea why my model does not work and it's like 50-50 chance to get it right. I'm getting really frustrated. Here is the code so far: ​ import pandas as pd import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import Dataset, DataLoader from collections import Counter from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score import re from imblearn.under_sampling import RandomUnderSampler # Loading the data file_path = "C:/Users/alex/Desktop/DATASET/malicious_phish.csv" data = pd.read_csv(file_path) # Filtering data filtered_data = data[data['type'].isin(['phishing', 'benign'])] # Undersampling the majority class rus = RandomUnderSampler(rand…  ( 12 min )

Pre-Training using 4bit (NOT fine-tunning) Hello community! I have been messing around with open source LLM's running them locally using peft and AutoGPTQ in Transformers. I even trained a few QLora models (my favorite part) However my question is this, given the performance of a 4bit model why hasn't there been any research in this area? Is it possible to even create a new model using 4bit altogether? I am sure it's not as easy as it sounds but I haven't seen anyone try. Just curious cause it will open doors for many of us with consumer grade hardware. Thanks! submitted by /u/Delicious-Farmer-234 [link] [comments]  ( 9 min )

https://github.com/michaelfeil/infinity Infinity, a open source REST API for serving vector embeddings, using a torch / ctranslate2 backend. Its under MIT License, fully tested and available under GitHub. I am the main author, curious to get your feedback. FYI: Huggingface launched a couple of days after me a similar project ("text-embeddings-inference"), under a non open-source and non-commercial license. submitted by /u/OrganicMesh [link] [comments]  ( 9 min )

Researchers from Yonsei University and UC Berkeley recently developed a new AI method for enabling autonomous robots to navigate unfamiliar environments filled with obstacles using only visual data as input. The key innovation is a customized diffusion model. Diffusion models can generate diverse motion plans by adding controlled noise. The researchers tailored the model to mimic how heat avoids insulation when dispersing through space. Similar to heat navigating around insulators, this "collision-avoiding" diffusion model learns to predict robot motions that avoid collisions with obstacles. It generates reachable goals and viable motion plans to those goals simultaneously. In simulations, this approach achieved ~98% success rates in navigating to target destinations while avoiding randomly generated obstacles using only visual map images as input. While extensive real-world testing is still needed (only 2D, only simulation), these initial results showcase promising capabilities: Enables navigation in unfamiliar environments without pre-mapping. Flexibly identifies and progresses toward reachable goals. Avoids unnecessary sensing systems for obstacle avoidance. Learns complex collision avoidance heuristics from visual data. I like the thermo + AI + robotics combination here - takes me back to my days in aerospace engineering. Pretty interesting approach. Full summary is here. Paper here. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

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@MetaGPT_ github.com/geekan/MetaGPT - multi agent collaboration - MetaGPT encodes Standard Operating Procedures (SOPs) into prompts. The claim is that it takes a one line requirement as input and outputs user stories / competitive analysis / requirements / data structures / APIs / documents, etc. @Ollama_ai github.com/jmorganca/olla… - run large language models locally. The future of AI/LLMs may not be on the cloud, but on your own laptops/mobiles. ollama.ai/blog/building-… @huggingface github.com/huggingface/ca… - slick ML framework for Rust with a focus on performance (including GPU support) @remilouf github.com/outlines-dev/o… - helps developers guide text generation to build robust interfaces with external systems. Provides generation methods that guarantee that the output will match a regular expressions, or follow a JSON schema. github.com/YiVal/YiVal enterprise AI platform submitted by /u/oana77oo [link] [comments]  ( 9 min )

Please post your questions here instead of creating a new thread. Encourage others who create new posts for questions to post here instead! Thread will stay alive until next one so keep posting after the date in the title. Thanks to everyone for answering questions in the previous thread! submitted by /u/AutoModerator [link] [comments]  ( 9 min )

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Hey guys, I found an interesting paper recently. Universities in the UK introduced Loop Copilot, enabling users to generate and iteratively refine music through an interactive, multi-round dialogue interface. Using language interaction to assist in composing music is very appealing, AI makes a complex workflow easy and automated. https://preview.redd.it/nzvlgevcarvb1.jpg?width=998&format=pjpg&auto=webp&s=815e0f48c299831700215ebdb4257423e317f5ec submitted by /u/xuying_li [link] [comments]  ( 9 min )

I am performing a (machine learning) time series forecast on monthly data from the last 20 years. If I separate my data into a train, validation, and test set, the validation set is almost completely filled with extreme values due to the Covid period. How to account for this? submitted by /u/Ambitious-Pay6329 [link] [comments]  ( 9 min )

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As the title says. Recently, I found a review paper where the authors showed an exponential growth of published papers related to ML or DL. I was wondering if you even read those. If yes what's your way to find good and reliable papers? Do you choose only ones with a significant number of citations? Or just strictly related to your field? If no, why not? https://preview.redd.it/jwjvej5f5qvb1.jpg?width=1080&format=pjpg&auto=webp&s=bf3f7e08e0fe09fe0c6a6fd8d194945b45f5858e submitted by /u/hahahaczyk [link] [comments]  ( 9 min )

I know that there are a lot of efforts at the moment to improve the algorithms used for landmine detection. Is anyone aware of any ongoing open-source projects in this space? submitted by /u/Eightstream [link] [comments]  ( 9 min )

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Researchers from Germany have developed DeepMB, a groundbreaking deep-learning framework enabling high-quality and real-time optoacoustic imaging via multispectral optoacoustic tomography (MSOT). With potentially transformative implications for health care, this innovation might redefine medical imaging standards. To stay ahead of developments in AI, look here first. DeepMB breakthrough DeepMB resolves the longstanding tradeoff between image quality and speed in medical imaging. The deep-learning framework uses a deep neural network for model-based reconstruction, allowing for fast, high-quality imaging. DeepMB can reconstruct images approximately 1000 times faster than conventional techniques, with virtually no loss in image quality. Impressive metrics and implications The researchers accomplished accurate optoacoustic image reconstruction in just 31 milliseconds per image by training the system to pairingly synthesize optoacoustic signals with ground-truth images. DeepMB promises to equip clinicians with immediate access to high-quality MSOT images, regardless of the patient's condition or scanned body area. The technology could extend to other imaging modalities, such as ultrasound, x-ray, and MRI, potentially changing how diseases are diagnosed and treated. Exciting prospects The development of DeepMB is a significant leap in optoacoustic imaging, promising to enhance healthcare outcomes. As DeepMB evolves, it could become integral to modern medical imaging, delivering high-quality results at previously unattainable speeds. (source) P.S. If you like this kind of analysis, I write a free newsletter that unpacks the most significant news and research in AI. Google, Meta, and OpenAI professionals are already subscribed submitted by /u/orthomax23 [link] [comments]  ( 9 min )

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I was previously using scale.ai but they have been extraordinarily slow. Does anyone have recommendations for services to label keypoints or pose? Bonus points if the labeling service is able to handle 3D / multi angle data coming from multiple cameras. I work in an academic lab and scale is <10k images per batch. submitted by /u/researchrig [link] [comments]  ( 9 min )

Hey, I want to make a text-to-song diff model, but I can't figure out the architecture I have already prepared a dataset of about 5000 songs of different genres, artists. It only contains the lyrics, the genre and the song itself Do I understand correctly that I should just encode the text and genre into one vector using CLIP and hope that the model will directly follow it (not skipping words and lines), or should I somehow make timestamps in the dataset (when, where and what text is sung)? I was inspired by Chirp V1 submitted by /u/Head-Selection-9785 [link] [comments]  ( 9 min )

IBM has developed a chip called NorthPole that runs AI-based image recognition 22 times faster than current chips on the market. The chip uses a two-dimensional array of memory blocks and interconnected CPUs to process data quickly. However, it can only run specialized AI processes and not training processes or large language models. The researchers plan to test connecting multiple NorthPole chips together to overcome this limitation. Source : https://techxplore.com/news/2023-10-ibm-northpole-chip-ai-based-image.html submitted by /u/NuseAI [link] [comments]

is there a website or some Ai to help me clean my inbox, stop receiving emails from certain senders etc etc... I've heard about: Sanebox for keeping your inbox organized Mailbutler for gathering contact details and tasks EmailTree for creating AI-powered workflows But they are paid and I'm looking for free alternatives submitted by /u/JOTA-137_0 [link] [comments]

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Based on input you have. This would be like an enterprise software level program I guess and you would input history and then through trawling through data locally it can generate diseases and probability patient has each disease based on data inputted Why doesn't something like this already exist? I am learning how to do differential diagnosis now and it seems use extremely rudimentary understanding of probability to diagnose things. You use clusters of symptoms and then use tests to eliminate stuff in the differential. It just seems like low hanging fruit that a program could do using tech we already have (I imagine LLMs will make it easier) submitted by /u/derpgod123 [link] [comments]

https://preview.redd.it/vi9wx005ksvb1.jpg?width=1024&format=pjpg&auto=webp&s=75502abcae7f2337693175101cb3491b8647d70d Revived an old script and made some images for it using Dall-E 3, just to test out the workflow: https://docs.google.com/document/d/1yyWRRmd0ah5Z4u8_aNYSq9csJ8pccP24Dcs9brPHbzs/edit Was pretty fun and I think by the end I got much better at learning how to maintain the consistency between characters, direct shots, etc. -~- submitted by /u/Kulimar [link] [comments]

Researchers from Yonsei University and UC Berkeley recently developed a new AI method for enabling autonomous robots to navigate unfamiliar environments filled with obstacles using only visual data as input. The key innovation is a customized diffusion model. Diffusion models can generate diverse motion plans by adding controlled noise. The researchers tailored the model to mimic how heat avoids insulation when dispersing through space. Similar to heat navigating around insulators, this "collision-avoiding" diffusion model learns to predict robot motions that avoid collisions with obstacles. It generates reachable goals and viable motion plans to those goals simultaneously. In simulations, this approach achieved ~98% success rates in navigating to target destinations while avoiding randomly generated obstacles using only visual map images as input. While extensive real-world testing is still needed (only 2D, only simulation), these initial results showcase promising capabilities: Enables navigation in unfamiliar environments without pre-mapping. Flexibly identifies and progresses toward reachable goals. Avoids unnecessary sensing systems for obstacle avoidance. Learns complex collision avoidance heuristics from visual data. I like the thermo + AI + robotics combination here - takes me back to my days in aerospace engineering. Pretty interesting approach. Full summary is here. Paper here. submitted by /u/Successful-Western27 [link] [comments]

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Quick update on my self-learning software experiment: Thanks to your feedback, I decided to test my prediction system on a newer tower-defense game from the Apple App Store (simply called ‘The Tower’). What's crucial to remember is that this system is not pre-trained and only learns from the current game it encounters - it starts with zero knowledge and learns exclusively from the game it's currently playing, building from the ground up without the use of deep learning or neural networks. In this game (unlike Airstriker which I’ve previously used), players don't control a spaceship or fire weapons (you play the game by ‘upgrading’ your weapons, etc.). It's simpler because there's only one type of enemy that always approaches the center, so the system cannot demonstrate its capabilities for differentiation in this case. But this simplicity presents some other interesting challenges: Enemies approach from all 360-degree directions, pushing the boundaries of the path prediction software. They overlap during explosions, demanding the system to separate them. There's also more visual clutter, including static lines and a non-black background. The system's predictive performance has been remarkably strong. I’ve put together an overlay video to visually demonstrate how the system learns and adapts in this new game. Note: If things don’t align perfectly in there, it’s due to my poor video editing skills… Your feedback is appreciated as always! submitted by /u/_timmah_ [link] [comments]

Without us, artificial intelligence just becomes intelligence submitted by /u/cognaceast [link] [comments]

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Let me clarify what I'm asking through an example: Artificial Intelligence in videogames has failed to develop in any meaningful way over the past two decades, at least as far as the typical end-user is concerned, and nowhere is this more apparent than in strategy games. Whether we're talking about the 90's or today, AI opponents typically have to receive significant cheats in order to provide a challenging experience for the player. This is widely considered undesirable, can harm immersion or a sense of fair-play, and leads to the concept of "cheesing" the AI (exploiting obvious weaknesses in the AI logic, something which is sometimes necessary if an AI receives such strong bonuses that any strategy you might attempt against another human player would be impossible to execute successfull…

Three weeks ago I wrote about supereggs, a shape popularized by Piet Hein. One aspect of supereggs that I did not address is their stability. Looking at the photo above, you could imagine that if you gave the object a slight nudge it would not fall over. Your intuition would be right: supereggs are stable. […] Stability of a superegg first appeared on John D. Cook.  ( 6 min )

Suppose that when Team X and Team Y play, the probability that X will win a single game is p and the probability that Y will win is q = 1 − p. What is the probability that X will win the majority of a series of N games for some odd number N? We know intuitively […] Best-of-five versus Best-of-seven first appeared on John D. Cook.  ( 5 min )

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Hi everyone, I'm new to robotic arms and I want to learn more about how to implement them using mujoco env. I'm looking for some open-source projects on github that I can run and understand. I tried MuJoCo_RL_UR5 repo but it didn't work well for me, it only deployed a random agent. Do you have any recommendations for good repos that are beginner-friendly and well-documented? submitted by /u/satyamstar [link] [comments]

After multiple iterations the value function converge by bellaman updates (vale iteration algorithm). Can someone provide a intuitive reasoning why the value converges? submitted by /u/RaceCondition01 [link] [comments]

TensorFlow Keras correcting camera horizon in AC Valhalla https://www.youtube.com/watch?v=ASy-2zOMj_Y submitted by /u/Kostiantyn-Dvornik [link] [comments]

Hello, I’m newbie in neural networks and I wonder, how do people decide how many hidden layers there will be and how many neurons will be inside? What the logic behind? submitted by /u/Particular-Song-633 [link] [comments]

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The rise of generative AI in search engines like Google threatens the $68 billion search engine optimization (SEO) industry. Generative AI tools like ChatGPT aim to provide direct answers to user queries, bypassing the need for users to click on search results. This could render SEO efforts useless and impact the revenues of SEO consultants and search engines. However, generative AI search engines still face challenges such as providing incorrect or plagiarized answers, and gaining user trust and loyalty. Search engines have been quick to experiment with generative AI to improve search results, with Google's Bard, Microsoft's Bing AI, Baidu's ERNIE, and DuckDuckGo's DuckAssist being examples of this approach. As the quality of AI-generated answers improves, users will have less incentive to browse through search result listings, impacting the revenues of SEO consultants and search engines. The SEO industry generated $68.1 billion globally in 2022 and was expected to reach $129.6 billion by 2030, but the emergence of generative AI puts the industry at risk of obsolescence. Generative AI search engines are still in their infancy and face challenges such as providing incorrect or plagiarized answers, limiting their trust and loyalty among users. However, with the resources available to researchers, it is safe to assume that generative AI models will improve over time, leading to the potential death of the SEO industry. Source : https://theconversation.com/why-google-bing-and-other-search-engines-embrace-of-generative-ai-threatens-68-billion-seo-industry-210243 submitted by /u/NuseAI [link] [comments]

Hi everyone, I recently undertook a personal project where I tried to automate the entire process of creating TikTok videos using various AI tools. The goal was to see how advanced we've come in terms of AI's capabilities in content creation and to explore the nuances of automating a traditionally 'human' task. Here's a brief breakdown: Scripting: Leveraged ChatGPT for generating video scripts. Voiceovers: Used ElevenLabs for lifelike voice narration. Video Creation: Employed a combination of StableDiffusion Animate & Replicate. Editing: Automated the editing process to sync with the AI-generated voiceovers. After setting everything up, I ran the system for a month, generating 3 videos daily. The results were intriguing and a mix of expected and unexpected outcomes. Would love to hear thoughts, feedback, or similar experiences from the community. Are there other creative ways you've seen or used AI in content creation? submitted by /u/General_crypto [link] [comments]

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The future of programming may involve human-like communication techniques, including yelling in all caps. OpenAI's DALL-E 3 AI image generator integrated into ChatGPT revealed internal prompts shared between the image generator and the AI assistant. The prompts included commands written in all-caps for emphasis. This shows that programming and communicating with computers may become more human-like in the future. Previously, programs used specialized data formats and APIs to communicate, but now large language models allow for cross-program interaction in conventional English. OpenAI trained GPT-4, the AI model used in ChatGPT DALL-E interface, on hundreds of millions of documents scraped from the web, which included instances of polite language and reactions to it. The use of all-caps in the DALL-E message is interpreted as emphasis, and the model pays more attention to capitalized sentences. In the future, programming and communicating with computers may involve more emphasis and human-like communication techniques. Source : https://arstechnica.com/information-technology/2023/10/thanks-to-ai-the-future-of-programming-may-involve-yelling-in-all-caps/ submitted by /u/NuseAI [link] [comments]

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This dating app SciMatch uses AI to find your soulmate by your face. Snap a selfie, and let the app do the rest.[1] The Biden administration is reducing the types of semiconductors that American companies will be able to sell to China, citing the desire to close loopholes in existing regulations announced last year.[2] Business Schools Are Adding AI Education Into The Curriculum.[3] Google Pixel’s face-altering photo tool sparks AI manipulation debate.[4] Sources: [1] https://www.foxnews.com/tech/dating-app-uses-ai-find-soul-mate-face [2] https://www.cnn.com/2023/10/18/tech/us-china-chip-export-curbs-intl-hnk/index.html [3] https://www.entrepreneur.com/business-news/business-schools-are-adding-ai-education-for-future-ceos/464054 [4] https://www.bbc.com/news/technology-67170014 submitted by /u/Excellent-Target-847 [link] [comments]

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My last post in this thread got almost 90k views, honestly I'm very happy that I was able to be so helpful. ​ One guy asked me why I couldn't give more details about what tools I use and what tools help me?:/ I decided to make the top 24 tools and describe what they are responsible for in 2 words. In order not to violate the rules of r/artificial I decided not to leave direct links to tools, so as not to violate the rules, as some tools can be paid, I left only links to 2 resources where I took this information, but they are fortunately free. YouTube Summaries → http://eightify.app 3D Animations → http://moviebot.io AI Assistant → http://zipzap.ai Prompts → http://wnr.ai How-to-videos → http://teachomatic.net Custom AI chatbots ➝ http://chatling.ai Remove Background ➝ http://unscreen.com Forms ➝ http://feathery.io Presentations ➝ http://beautiful.ai Learning ➝ http://albus.org Blog ➝ http://jasper.ai Videos ➝ http://descript.com Image ➝ http://tryleap.ai Resume ➝ http://mosaicml.com Grammar Check ➝ http://trinka.ai Meeting ➝ http://krisp.ai Video ➝ http://decoherence.co App development ➝ http://brancher.ai Design ➝ http://modiphy.com Coding assistant ➝ http://bito.ai Twitter assistant ➝ http://tweethunter.io Personal assistant ➝ http://chat.openai.com LinkedIn assistant ➝ http://taplio.com YouTube assistant ➝ http://vidiq.com I hope this is as useful to you as the first post I'm just sharing my experiences and observations in the field of ai. LIST AND SITE https://preview.redd.it/zgkra3plpgvb1.jpg?width=1068&format=pjpg&auto=webp&s=779003d65dfa70c58d50ad690a0e436c735cdaeb submitted by /u/PerceptionPlayful469 [link] [comments]

Oracle has partnered with Nvidia to bring Nvidia's AI stack to its marketplace, giving Oracle customers access to top-of-the-line GPUs for training models and building generative applications. Eligible enterprises can purchase Nvidia's DGX Cloud AI supercomputing platform and AI Enterprise software directly from the marketplace and start training models for deployment on the Oracle Cloud Infrastructure. Nvidia DGX Cloud offers a serverless experience for multi-node training of custom generative AI models, supporting near-limitless scale of GPU resources. Nvidia AI Enterprise helps teams accelerate the deployment of models to production, with features such as the Nvidia NeMo framework, Rapids, TensorRT LLM open-source library, and Triton Inference server. Oracle has been focused on industry partnerships for its AI efforts and has announced generative AI capabilities in its products and solutions. Source : https://venturebeat.com/ai/oracle-loops-in-nvidias-ai-stack-for-end-to-end-model-development/ submitted by /u/NuseAI [link] [comments]

I know that given a policy, I can find the value function using iterative policy evaluation. Can I, given the value function, find the policy? submitted by /u/MomoSolar [link] [comments]

Hi, I've been re-reading the "What matters in on-policy reinforcement learning" paper (https://arxiv.org/abs/2006.05990), and noticed that they suggest to recompute advantages at the beginning of each epoch (choice C5, see section 3.5 and appendix B.1). I was wondering: if someone here had already tried this and seen a significant improvement (which is what the paper suggests) ? if it did not also suppose to recompute the value targets at the beginning of each epoch, which could lead to some sort of moving target issue ? Best, submitted by /u/Scrimbibete [link] [comments]

Let us say we are building a model that will learn how to play a computer game like DOTA or league of legends. If model for example, buys weapon A, and use the item's ability on opponent B, it should learn what damage it gives to opponent given the items opponent B is wearing. But we would have done a lot of other actions in between before being able to use that weapon to reward the model on what it does / how much damage it made. How does do you do delayed reward for specific action made X number of steps ago? Thank you. submitted by /u/oniongarlic88 [link] [comments]

Zoomposium with Professor Dr. Petra Ritter: "The simulation of brains" In another installment in our "Zoomposium Series" on the topic of "Brain Research", my colleague Axel Stöcker of the "Blog der großen Fragen" and I had the great honor and pleasure of conducting an interview with the very well-known and renowned German medical doctor and neuroscientist Professor Dr. Petra Ritter. In this context, Ms. Ritter became a co-founder and leader of the co-design project "The Virtual Brain", which is a component of the European Open Science Cloud (EOSC) and is "a neuroinformatics platform for simulating whole brain networks using biologically realistic connectivity". She is leading the development of a virtual research environment as a collaborative research platform for sensitive health data and head of the "German National Neuroscience Research Infrastructure Initiative (NFDI-Neuroscince)" and involved in the development of the "Health Data Cloud EBRAINS". Petra Ritter has been Johanna Quandt Professor and Head of the Section for Brain Simulation at the Department of Neurology with Experimental Neurology at Charité - Universitätsmedizin Berlin since 2017. There, Professor Ritter and her team are involved in the "Simulation of Brains". More at: https://philosophies.de/index.php/2023/09/17/die-simulation-von-gehirnen/ ​ https://preview.redd.it/937m7mtyvivb1.jpg?width=1000&format=pjpg&auto=webp&s=22d1a7576f2ebbe7904f0187bd7c0234df7ddb8f submitted by /u/philosophiesde [link] [comments]

Hey there mates, I am a 3rd year PhD student, trying to break into good quality research (tired of trying different permutations ans combinations of X and Ys, and hitting dead end when things don't work, or worse -- being unable to explain why things work :D). I have recently decided to read PRML cover to cover (slowly) and do some of the exercises as well. Goal is to finish in 6 months (2 chapters per month). Is there anyone on a similar journey, would love to tag along and discuss nuances? submitted by /u/Zealousideal_Yak9131 [link] [comments]  ( 9 min )

About the latest Jason Wei’s tweet. submitted by /u/mildlyphd [link] [comments]  ( 9 min )

What are some of the best physics engine that we should be using to implement physics for complex reinforcement related tasks(like humanoid motions) ?? I came across mujoco, physx , pybullet, issac etc but not sure which to go with. Isaac seems to be something very interesting but the minimun requirements as per the website is 32gb of RAM which is way to much for me (I use a 8gb one). mujoco is good but the docs are very confusing and hard to get through. what do you believe is the best choice to go with?? submitted by /u/rakk109 [link] [comments]  ( 9 min )

This crossed my mind recently and after searching online I couldn't find a concrete answer: would an ensemble composed of strong predictors (let's say training on 1 model of that type had a high metric performance) perform better than an ensemble composed of weak predictors? Bonus: are there any resources that would support your position you can link below? submitted by /u/robml [link] [comments]  ( 9 min )

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Here's my implementation of an idea I had many years ago: a Sensorimotor Inference Engine. A machine that explores the states space of an environment, learning how to traverse the state space, learning how to manipulate the environment, which when given a goal can manipulate the environment in accordance to the goal. In other words, it's an agent which learns not one policy, but all possible policies. Doing so, I believe requires a hierarchy: layers of the same structure which learn broader and broader contexts of the environment. I have recently attempted to design an extremely simple, and modularized version of this agent: The Encoder-Predictor-Actor circuit. I need feedback, do you think it would work? if it might work, how might I train the Actor model? I think I know how to train the Encoder and Predictor models, but the Actor model will be harder to train, so if you have any ideas I'd love to hear from you! ps. sorry for the typos in the image text. a first-pass diagram of the 'simplest' implementation of a sensorimotor inference engine: the encoder-predictor-actor circuit submitted by /u/Stack3 [link] [comments]  ( 9 min )

Hi there, I need some suggestions from you experts. I am an aerospace engineer (both BSc and MSc), with a university minor in AI. It's pretty clear to me that I should have studied computer science given my passion for this world. In the last 4 years I worked as engineer in a major aerospace company, and I managed to get back on track with computer science and ML by working as a data scientist and doing ML projects applied to space, while also practicing with LLM agents. My dream is to enter the AGI world, maybe working as an "AI engineer", or working on creating true "autonomous" systems, leveraging multi-modal models maybe. What do you suggest I should focus on to reach this goal? Getting first some "credit" as an ML engineer though courses and certifications, open source projects, or maybe applying right now to some startups in the field? Thanks guys! submitted by /u/cappellino1 [link] [comments]  ( 9 min )

We’ve been diving deep into Arxiv Papers as a team on Fridays, hope it’s helpful and feel free to join live if you like the format! submitted by /u/FallMindless3563 [link] [comments]  ( 9 min )

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I am taking the computer vision course at my university. We have to do a final project but I am unable to come up with concrete ideas. These are the options: • Select a paper from the computer vision literature, implement and test the approach described in that paper • Take publicly available code, apply it to an interesting novel dataset and explore various extensions and modifications. You may also want to compare two or more systems. Running existing code on the data provided by the authors is not sufficient. • Design and implement a solution to a problem that interests you. This may earn you extra credits. Can anyone please help with what to do? submitted by /u/kxenak [link] [comments]  ( 9 min )

I am on a computer vision algorithm and I will be benchmarking my method on the MS COCO dataset, like the other methods that have been proposed for the same problem. I want to know if I can use a smaller dataset (COCO minitrain) for my ablation experiments to demonstrate the efficacy of the different components used in my algorithm and to save time and cost, or will that be a red flag to journal reviewers? submitted by /u/notEVOLVED [link] [comments]  ( 9 min )

I am embedding with gpt and upserting large medical reports into pinecone and then would like to query for chronological result. For example, I upload a report that consists of 10 office visits. I would like to know the date and results of the first visit and then the last visit. when I embed a query containing: How did the patient describe their pain in the last office visit in the text? pinecone doesn't understand the context of 'last' since it is just doing cosine likeness. It pulls pain information but doesn't have a clue which comes first. Any help would be greatly appreciated. submitted by /u/Silent_Case_3058 [link] [comments]  ( 9 min )

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Hi, Has anyone done RAG on a multi level tabular data? If yes then what problems have you faced and how did you solve those? My model gives better answers when I converted the data to a JSON and then embedded it. But I'm looking for a better approach. submitted by /u/Euphoric-Chart1428 [link] [comments]  ( 9 min )

I was wondering after reading the recent streamingLLM paper https://arxiv.org/pdf/2309.17453.pdf if the attention sink they use through pre-training and inference is analogous to the learnable padding used in the MEGABYTE architecture https://arxiv.org/pdf/2305.07185.pdf although used for a different purpose? So if I just used MEGABYTE with sliding window attention at inference would it be the same as streamingLLM? submitted by /u/Additional-Ad-7043 [link] [comments]  ( 9 min )

I need a new PC to run NN on. My training sets are about 50GB. Would I be best building my own, or using Google colab pro? Anyone know the specs equivalent to colab Pro? submitted by /u/ajplant [link] [comments]  ( 9 min )

I want to create a research proposal in this area. Ideally, I would like to work towards self-supervised models that take as input raw (not preprocessed) data of various modalities (text, image, video, audio, ...) and output a knowledge graph of all the data contained within. For example, I could feed it the Wikipedia article about dogs and it spits back all the information contained within, structured in the form of a graph. For people who work in the same general area can you point me to the SOTA models/efforts and research groups that work in this area? And can you also highlight the current challenges to be overcome, if you are deep enough to know? ​ submitted by /u/KlutzyBiz [link] [comments]

Hi. I am working on TokenClassification problem which requires significant language understanding in the base model and was wondering if:- Is there any research that has shown on multiple datasets that encoder-only pretraining tasks produce more optimal results when finetuned for Token Classification tasks compared to decoder-only with same parameter sized models. Since a lot of LLM research is focused on text generation, most model are trained on decoder-only pretraining tasks, so what is the largest encoder-only pretrained model that is trained on >1T tokens. If encoder-only models do indeed produce more optimal results for Token Classification is there any empirical rule w.r.t. to parameter size that we can expect decoder-only to outperform encode-only models. (Eg. say 3B decoder-only is equivalent to 1B encoder-only with similar pretraining and finetuning data) submitted by /u/RemoteSaint [link] [comments]  ( 9 min )

Hi everyone. I would just like to discuss a few things. I've spent about 2 months studying CNNs on coursera from the Deep Learning Specialization. In this time period I learnt the fundamentals and mechanisms of how CNNs work. I also took lectures on a few research papers that studied a few classical CNN models like AlexNet, LeNet-5, VGG-16. And then a few research papers that studied advanced stuff like ResNets, Inception Network, MobileNet, EfficientNet etc. Following that I studied Detection Algorithms, with a primary focus on YOLO Algorithm. I also briefly studied Regional Proposals, Semantic Segmentation, R-CNN, Fast-RCNN, Faster R-CNN, U-Net. I also learnt Face Recognition and Verification Models like Siamese Network using Triplet Loss function and Binary Classification. And also cove…  ( 10 min )

Github: GPT-4V-Act (A demo video can be found on the Github) Hi there! I'd like to share with you a project I recently developed. My inspiration came from a recent post about Set-of-Mark visual grounding in GPT-4V. Fascinatingly, my tests showed that GPT-4V, equipped with this capability, could inspect a UI screenshot and provide the precise pixel coordinates needed for steering a mouse/keyboard to perform a specified task. Motivated by this, I built a proof-of-concept web browser embedded with a co-pilot that can "view" the browser and interact with it. Currently, the demo is basic, utilizing web-scraping to morph ChatGPT Plus into an unofficial GPT-4V API at the backend. It lacks some actions and an adblock, resulting in the agent potentially being overloaded by the extensive popups …  ( 10 min )

I recently wrote the book “AI & Data Literacy: Empowering Citizens of Data Science” to help non-data scientists – which is most of the world – understand the risks associated with how companies capture and use your personal data to influence your viewing and buying habits… and even your political and societal beliefs.  And while… Read More »Grade School & Preteen AI & Data Literacy The post Grade School & Preteen AI & Data Literacy appeared first on Data Science Central.  ( 22 min )

​ Generating a Wide Variety of Sounds I'm a non-technical person with very little knowledge to develop AI tools and intending to learn Python and based on that My question is as follows: ​ Are there tools or chatgpt like platforms that can help people like me to generate couple of sounds like dog barks, cat meows. I want either something that can generate a variety of sounds or I want to work towards making something that cane help me generate audios like dog barks, such as fierce, aggressive ones but not just limited to dog barks but also sound focused on nature, other animals, vehicles, machinery(e.g., honks, engine sounds ), and possibly human sounds (though that's not my primary focus for now). The amount of technical Assistance Needed I also came across a tool like Teachable Machine and was wondering if it could be a solution as it does offer tools for audio. I am also aware that I would need datasets for such a task but apart from that I am not too sure about the nitty gritty or should I say the intricacies involved as well as the knowledge needed as I do assume it is likely not very easy https://www.youtube.com/watch?v=L4GOmYPPqn8&t=1854s ​ [Teachable Machine](https://teachablemachine.withgoogle.com/) ​ Inspiration I was inspired by a project I found here: [https://x.com/TheAIAnonGuy/status/1684443155448360961?s=20] ​ ​ Can anyone provide insights, guidance, or recommendations on how to accomplish this? To be fair, I'm not really sure if this is an audio-related or neural/machine learning (ML)/deep learning related learning question. But I would like more insight if this is possible on an individual scale either with teachable, code or AI or a combination of all approaches and if there are any beginner friendly ways to achieve this Thank you all for your assistance! submitted by /u/Beginning_Finding_98 [link] [comments]

Domain incremental learning aims to adapt to a sequence of domains with access to only a small subset of data (i.e., memory) from previous domains. Various methods have been proposed for this problem, but it is still unclear how they are related and when practitioners should choose one method over another. In response, we propose a unified framework, dubbed Unified Domain Incremental Learning (UDIL), for domain incremental learning with memory. Our UDIL **unifies** various existing methods, and our theoretical analysis shows that UDIL always achieves a tighter generalization error bound compared to these methods. The key insight is that different existing methods correspond to our bound with different **fixed** coefficients; based on insights from this unification, our UDIL allows **adaptive** coefficients during training, thereby always achieving the tightest bound. Empirical results show that our UDIL outperforms the state-of-the-art domain incremental learning methods on both synthetic and real-world datasets. Code will be available at https://github.com/Wang-ML-Lab/unified-continual-learning.  ( 2 min )

Significant computational resources are required to train Graph Neural Networks (GNNs) at a large scale, and the process is highly data-intensive. One of the most effective ways to reduce resource requirements is minibatch training coupled with graph sampling. GNNs have the unique property that items in a minibatch have overlapping data. However, the commonly implemented Independent Minibatching approach assigns each Processing Element (PE) its own minibatch to process, leading to duplicated computations and input data access across PEs. This amplifies the Neighborhood Explosion Phenomenon (NEP), which is the main bottleneck limiting scaling. To reduce the effects of NEP in the multi-PE setting, we propose a new approach called Cooperative Minibatching. Our approach capitalizes on the fact that the size of the sampled subgraph is a concave function of the batch size, leading to significant reductions in the amount of work per seed vertex as batch sizes increase. Hence, it is favorable for processors equipped with a fast interconnect to work on a large minibatch together as a single larger processor, instead of working on separate smaller minibatches, even though global batch size is identical. We also show how to take advantage of the same phenomenon in serial execution by generating dependent consecutive minibatches. Our experimental evaluations show up to 4x bandwidth savings for fetching vertex embeddings, by simply increasing this dependency without harming model convergence. Combining our proposed approaches, we achieve up to 64% speedup over Independent Minibatching on single-node multi-GPU systems.  ( 3 min )

Pretrained transformer models have demonstrated remarkable performance across various natural language processing tasks. These models leverage the attention mechanism to capture long- and short-range dependencies in the sequence. However, the (full) attention mechanism incurs high computational cost - quadratic in the sequence length, which is not affordable in tasks with long sequences, e.g., inputs with 8k tokens. Although sparse attention can be used to improve computational efficiency, as suggested in existing work, it has limited modeling capacity and often fails to capture complicated dependencies in long sequences. To tackle this challenge, we propose MASFormer, an easy-to-implement transformer variant with Mixed Attention Spans. Specifically, MASFormer is equipped with full attention to capture long-range dependencies, but only at a small number of layers. For the remaining layers, MASformer only employs sparse attention to capture short-range dependencies. Our experiments on natural language modeling and generation tasks show that a decoder-only MASFormer model of 1.3B parameters can achieve competitive performance to vanilla transformers with full attention while significantly reducing computational cost (up to 75%). Additionally, we investigate the effectiveness of continual training with long sequence data and how sequence length impacts downstream generation performance, which may be of independent interest.  ( 2 min )

In theoretical ML, the teacher-student paradigm is often employed as an effective metaphor for real-life tuition. The above scheme proves particularly relevant when the student network is overparameterized as compared to the teacher network. Under these operating conditions, it is tempting to speculate that the student ability to handle the given task could be eventually stored in a sub-portion of the whole network. This latter should be to some extent reminiscent of the frozen teacher structure, according to suitable metrics, while being approximately invariant across different architectures of the student candidate network. Unfortunately, state-of-the-art conventional learning techniques could not help in identifying the existence of such an invariant subnetwork, due to the inherent degree of non-convexity that characterizes the examined problem. In this work, we take a leap forward by proposing a radically different optimization scheme which builds on a spectral representation of the linear transfer of information between layers. The gradient is hence calculated with respect to both eigenvalues and eigenvectors with negligible increase in terms of computational and complexity load, as compared to standard training algorithms. Working in this framework, we could isolate a stable student substructure, that mirrors the true complexity of the teacher in terms of computing neurons, path distribution and topological attributes. When pruning unimportant nodes of the trained student, as follows a ranking that reflects the optimized eigenvalues, no degradation in the recorded performance is seen above a threshold that corresponds to the effective teacher size. The observed behavior can be pictured as a genuine second-order phase transition that bears universality traits.  ( 3 min )

Textural Inversion, a prompt learning method, learns a singular embedding for a new "word" to represent image style and appearance, allowing it to be integrated into natural language sentences to generate novel synthesised images. However, identifying and integrating multiple object-level concepts within one scene poses significant challenges even when embeddings for individual concepts are attainable. This is further confirmed by our empirical tests. To address this challenge, we introduce a framework for Multi-Concept Prompt Learning (MCPL), where multiple new "words" are simultaneously learned from a single sentence-image pair. To enhance the accuracy of word-concept correlation, we propose three regularisation techniques: Attention Masking (AttnMask) to concentrate learning on relevant areas; Prompts Contrastive Loss (PromptCL) to separate the embeddings of different concepts; and Bind adjective (Bind adj.) to associate new "words" with known words. We evaluate via image generation, editing, and attention visualisation with diverse images. Extensive quantitative comparisons demonstrate that our method can learn more semantically disentangled concepts with enhanced word-concept correlation. Additionally, we introduce a novel dataset and evaluation protocol tailored for this new task of learning object-level concepts.  ( 2 min )

Bilateral trade revolves around the challenge of facilitating transactions between two strategic agents -- a seller and a buyer -- both of whom have a private valuations for the item. We study the online version of the problem, in which at each time step a new seller and buyer arrive. The learner's task is to set a price for each agent, without any knowledge about their valuations. The sequence of sellers and buyers is chosen by an oblivious adversary. In this setting, known negative results rule out the possibility of designing algorithms with sublinear regret when the learner has to guarantee budget balance for each iteration. In this paper, we introduce the notion of global budget balance, which requires the agent to be budget balance only over the entire time horizon. By requiring global budget balance, we provide the first no-regret algorithms for bilateral trade with adversarial inputs under various feedback models. First, we show that in the full-feedback model the learner can guarantee $\tilde{O}(\sqrt{T})$ regret against the best fixed prices in hindsight, which is order-wise optimal. Then, in the case of partial feedback models, we provide an algorithm guaranteeing a $\tilde{O}(T^{3/4})$ regret upper bound with one-bit feedback, which we complement with a nearly-matching lower bound. Finally, we investigate how these results vary when measuring regret using an alternative benchmark.  ( 2 min )

The growing adoption of declarative software specification languages, coupled with their inherent difficulty in debugging, has underscored the need for effective and automated repair techniques applicable to such languages. Researchers have recently explored various methods to automatically repair declarative software specifications, such as template-based repair, feedback-driven iterative repair, and bounded exhaustive approaches. The latest developments in large language models provide new opportunities for the automatic repair of declarative specifications. In this study, we assess the effectiveness of utilizing OpenAI's ChatGPT to repair software specifications written in the Alloy declarative language. Unlike imperative languages, specifications in Alloy are not executed but rather translated into logical formulas and evaluated using backend constraint solvers to identify specification instances and counterexamples to assertions. Our evaluation focuses on ChatGPT's ability to improve the correctness and completeness of Alloy declarative specifications through automatic repairs. We analyze the results produced by ChatGPT and compare them with those of leading automatic Alloy repair methods. Our study revealed that while ChatGPT falls short in comparison to existing techniques, it was able to successfully repair bugs that no other technique could address. Our analysis also identified errors in ChatGPT's generated repairs, including improper operator usage, type errors, higher-order logic misuse, and relational arity mismatches. Additionally, we observed instances of hallucinations in ChatGPT-generated repairs and inconsistency in its results. Our study provides valuable insights for software practitioners, researchers, and tool builders considering ChatGPT for declarative specification repairs.  ( 3 min )

We address the challenge of tracking an unknown number of targets in strong clutter environments using measurements from a radar sensor. Leveraging the range-Doppler spectra information, we identify the measurement classes, which serve as additional information to enhance clutter rejection and data association, thus bolstering the robustness of target tracking. We first introduce a novel neural enhanced message passing approach, where the beliefs obtained by the unified message passing are fed into the neural network as additional information. The output beliefs are then utilized to refine the original beliefs. Then, we propose a classification-aided robust multiple target tracking algorithm, employing the neural enhanced message passing technique. This algorithm is comprised of three modules: a message-passing module, a neural network module, and a Dempster-Shafer module. The message-passing module is used to represent the statistical model by the factor graph and infers target kinematic states, visibility states, and data associations based on the spatial measurement information. The neural network module is employed to extract features from range-Doppler spectra and derive beliefs on whether a measurement is target-generated or clutter-generated. The Dempster-Shafer module is used to fuse the beliefs obtained from both the factor graph and the neural network. As a result, our proposed algorithm adopts a model-and-data-driven framework, effectively enhancing clutter suppression and data association, leading to significant improvements in multiple target tracking performance. We validate the effectiveness of our approach using both simulated and real data scenarios, demonstrating its capability to handle challenging tracking scenarios in practical radar applications.  ( 3 min )

With the development of large language models (LLMs), striking a balance between the performance and safety of AI systems has never been more critical. However, the inherent tension between the objectives of helpfulness and harmlessness presents a significant challenge during LLM training. To address this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly decouples human preferences regarding helpfulness and harmlessness, effectively avoiding the crowdworkers' confusion about the tension and allowing us to train separate reward and cost models. We formalize the safety concern of LLMs as an optimization task of maximizing the reward function while satisfying specified cost constraints. Leveraging the Lagrangian method to solve this constrained problem, Safe RLHF dynamically adjusts the balance between the two objectives during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we demonstrate a superior ability to mitigate harmful responses while enhancing model performance compared to existing value-aligned algorithms. Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with collected human preferences, significantly improving its helpfulness and harmlessness according to human evaluations.  ( 2 min )

Pulsar timing arrays (PTAs) perform Bayesian posterior inference with expensive MCMC methods. Given a dataset of ~10-100 pulsars and O(10^3) timing residuals each, producing a posterior distribution for the stochastic gravitational wave background (SGWB) can take days to a week. The computational bottleneck arises because the likelihood evaluation required for MCMC is extremely costly when considering the dimensionality of the search space. Fortunately, generating simulated data is fast, so modern simulation-based inference techniques can be brought to bear on the problem. In this paper, we demonstrate how conditional normalizing flows trained on simulated data can be used for extremely fast and accurate estimation of the SGWB posteriors, reducing the sampling time from weeks to a matter of seconds.  ( 2 min )

Among the many variants of graph neural network (GNN) architectures capable of modeling data with cross-instance relations, an important subclass involves layers designed such that the forward pass iteratively reduces a graph-regularized energy function of interest. In this way, node embeddings produced at the output layer dually serve as both predictive features for solving downstream tasks (e.g., node classification) and energy function minimizers that inherit desirable inductive biases and interpretability. However, scaling GNN architectures constructed in this way remains challenging, in part because the convergence of the forward pass may involve models with considerable depth. To tackle this limitation, we propose a sampling-based energy function and scalable GNN layers that iteratively reduce it, guided by convergence guarantees in certain settings. We also instantiate a full GNN architecture based on these designs, and the model achieves competitive accuracy and scalability when applied to the largest publicly-available node classification benchmark exceeding 1TB in size.  ( 2 min )

Score-based generative models (SGMs) sample from a target distribution by iteratively transforming noise using the score function of the perturbed target. For any finite training set, this score function can be evaluated in closed form, but the resulting SGM memorizes its training data and does not generate novel samples. In practice, one approximates the score by training a neural network via score-matching. The error in this approximation promotes generalization, but neural SGMs are costly to train and sample, and the effective regularization this error provides is not well-understood theoretically. In this work, we instead explicitly smooth the closed-form score to obtain an SGM that generates novel samples without training. We analyze our model and propose an efficient nearest-neighbor-based estimator of its score function. Using this estimator, our method achieves sampling times competitive with neural SGMs while running on consumer-grade CPUs.  ( 2 min )

The burdensome impact of a skewed judges-to-cases ratio on the judicial system manifests in an overwhelming backlog of pending cases alongside an ongoing influx of new ones. To tackle this issue and expedite the judicial process, the proposition of an automated system capable of suggesting case outcomes based on factual evidence and precedent from past cases gains significance. This research paper centres on developing a graph neural network-based model to address the Legal Judgment Prediction (LJP) problem, recognizing the intrinsic graph structure of judicial cases and making it a binary node classification problem. We explored various embeddings as model features, while nodes such as time nodes and judicial acts were added and pruned to evaluate the model's performance. The study is done while considering the ethical dimension of fairness in these predictions, considering gender and name biases. A link prediction task is also conducted to assess the model's proficiency in anticipating connections between two specified nodes. By harnessing the capabilities of graph neural networks and incorporating fairness analyses, this research aims to contribute insights towards streamlining the adjudication process, enhancing judicial efficiency, and fostering a more equitable legal landscape, ultimately alleviating the strain imposed by mounting case backlogs. Our best-performing model with XLNet pre-trained embeddings as its features gives the macro F1 score of 75% for the LJP task. For link prediction, the same set of features is the best performing giving ROC of more than 80%

Spider silks are remarkable materials characterized by superb mechanical properties such as strength, extensibility and lightweightedness. Yet, to date, limited models are available to fully explore sequence-property relationships for analysis and design. Here we propose a custom generative large-language model to enable design of novel spider silk protein sequences to meet complex combinations of target mechanical properties. The model, pretrained on a large set of protein sequences, is fine-tuned on ~1,000 major ampullate spidroin (MaSp) sequences for which associated fiber-level mechanical properties exist, to yield an end-to-end forward and inverse generative strategy. Performance is assessed through: (1), a novelty analysis and protein type classification for generated spidroin sequences through BLAST searches, (2) property evaluation and comparison with similar sequences, (3) comparison of molecular structures, as well as, and (4) a detailed sequence motif analyses. We generate silk sequences with property combinations that do not exist in nature, and develop a deep understanding the mechanistic roles of sequence patterns in achieving overarching key mechanical properties (elastic modulus, strength, toughness, failure strain). The model provides an efficient approach to expand the silkome dataset, facilitating further sequence-structure analyses of silks, and establishes a foundation for synthetic silk design and optimization.

Given the ubiquitous use of tabular data in industries and the growing concerns in data privacy and security, tabular data synthesis emerges as a critical research area. The recent state-of-the-art methods show that large language models (LLMs) can be adopted to generate realistic tabular data. As LLMs pre-process tabular data as full text, they have the advantage of avoiding the curse of dimensionality associated with one-hot encoding high-dimensional data. However, their long training time and limited re-usability on new tasks prevent them from replacing exiting tabular generative models. In this paper, we propose Tabula, a tabular data synthesizer based on the language model structure. Through Tabula, we demonstrate the inherent limitation of employing pre-trained language models designed for natural language processing (NLP) in the context of tabular data synthesis. Our investigation delves into the development of a dedicated foundational model tailored specifically for tabular data synthesis. Additionally, we propose a token sequence compression strategy to significantly reduce training time while preserving the quality of synthetic data. Extensive experiments on six datasets demonstrate that using a language model structure without loading the well-trained model weights yields a better starting model for tabular data synthesis. Moreover, the Tabula model, previously trained on other tabular data, serves as an excellent foundation model for new tabular data synthesis tasks. Additionally, the token sequence compression method substantially reduces the model's training time. Results show that Tabula averagely reduces 46.2% training time per epoch comparing to current LLMs-based state-of-the-art algorithm and consistently achieves even higher synthetic data utility.

We study a distributed multi-armed bandit setting among a population of $n$ memory-constrained nodes in the gossip model: at each round, every node locally adopts one of $m$ arms, observes a reward drawn from the arm's (adversarially chosen) distribution, and then communicates with a randomly sampled neighbor, exchanging information to determine its policy in the next round. We introduce and analyze several families of dynamics for this task that are decentralized: each node's decision is entirely local and depends only on its most recently obtained reward and that of the neighbor it sampled. We show a connection between the global evolution of these decentralized dynamics with a certain class of "zero-sum" multiplicative weights update algorithms, and we develop a general framework for analyzing the population-level regret of these natural protocols. Using this framework, we derive sublinear regret bounds under a wide range of parameter regimes (i.e., the size of the population and number of arms) for both the stationary reward setting (where the mean of each arm's distribution is fixed over time) and the adversarial reward setting (where means can vary over time). Further, we show that these protocols can approximately optimize convex functions over the simplex when the reward distributions are generated from a stochastic gradient oracle.

Link prediction is a common task on graph-structured data that has seen applications in a variety of domains. Classically, hand-crafted heuristics were used for this task. Heuristic measures are chosen such that they correlate well with the underlying factors related to link formation. In recent years, a new class of methods has emerged that combines the advantages of message-passing neural networks (MPNN) and heuristics methods. These methods perform predictions by using the output of an MPNN in conjunction with a "pairwise encoding" that captures the relationship between nodes in the candidate link. They have been shown to achieve strong performance on numerous datasets. However, current pairwise encodings often contain a strong inductive bias, using the same underlying factors to classify all links. This limits the ability of existing methods to learn how to properly classify a variety of different links that may form from different factors. To address this limitation, we propose a new method, LPFormer, which attempts to adaptively learn the pairwise encodings for each link. LPFormer models the link factors via an attention module that learns the pairwise encoding that exists between nodes by modeling multiple factors integral to link prediction. Extensive experiments demonstrate that LPFormer can achieve SOTA performance on numerous datasets while maintaining efficiency.

Knowledge graph embedding models (KGEMs) have gained considerable traction in recent years. These models learn a vector representation of knowledge graph entities and relations, a.k.a. knowledge graph embeddings (KGEs). Learning versatile KGEs is desirable as it makes them useful for a broad range of tasks. However, KGEMs are usually trained for a specific task, which makes their embeddings task-dependent. In parallel, the widespread assumption that KGEMs actually create a semantic representation of the underlying entities and relations (e.g., project similar entities closer than dissimilar ones) has been challenged. In this work, we design heuristics for generating protographs -- small, modified versions of a KG that leverage RDF/S information. The learnt protograph-based embeddings are meant to encapsulate the semantics of a KG, and can be leveraged in learning KGEs that, in turn, also better capture semantics. Extensive experiments on various evaluation benchmarks demonstrate the soundness of this approach, which we call Modular and Agnostic SCHema-based Integration of protograph Embeddings (MASCHInE). In particular, MASCHInE helps produce more versatile KGEs that yield substantially better performance for entity clustering and node classification tasks. For link prediction, using MASCHinE substantially increases the number of semantically valid predictions with equivalent rank-based performance.

Knitted sensors frequently suffer from inconsistencies due to innate effects such as offset, relaxation, and drift. These properties, in combination, make it challenging to reliably map from sensor data to physical actuation. In this paper, we demonstrate a method for counteracting this by applying processing using a minimal artificial neural network (ANN) in combination with straightforward pre-processing. We apply a number of exponential smoothing filters on a re-sampled sensor signal, to produce features that preserve different levels of historical sensor data and, in combination, represent an adequate state of previous sensor actuation. By training a three-layer ANN with a total of 8 neurons, we manage to significantly improve the mapping between sensor reading and actuation force. Our findings also show that our technique translates to sensors of reasonably different composition in terms of material and structure, and it can furthermore be applied to related physical features such as strain.

In an era where symbolic mathematical equations are indispensable for modeling complex natural phenomena, scientific inquiry often involves collecting observations and translating them into mathematical expressions. Recently, deep learning has emerged as a powerful tool for extracting insights from data. However, existing models typically specialize in either numeric or symbolic domains, and are usually trained in a supervised manner tailored to specific tasks. This approach neglects the substantial benefits that could arise from a task-agnostic unified understanding between symbolic equations and their numeric counterparts. To bridge the gap, we introduce SNIP, a Symbolic-Numeric Integrated Pre-training, which employs joint contrastive learning between symbolic and numeric domains, enhancing their mutual similarities in the pre-trained embeddings. By performing latent space analysis, we observe that SNIP provides cross-domain insights into the representations, revealing that symbolic supervision enhances the embeddings of numeric data and vice versa. We evaluate SNIP across diverse tasks, including symbolic-to-numeric mathematical property prediction and numeric-to-symbolic equation discovery, commonly known as symbolic regression. Results show that SNIP effectively transfers to various tasks, consistently outperforming fully supervised baselines and competing strongly with established task-specific methods, especially in few-shot learning scenarios where available data is limited.

Bayesian optimization (BO) is widely used for black-box optimization problems, and have been shown to perform well in various real-world tasks. However, most of the existing BO methods aim to learn the optimal solution, which may become infeasible when the parameter space is extremely large or the problem is time-sensitive. In these contexts, switching to a satisficing solution that requires less information can result in better performance. In this work, we focus on time-sensitive black-box optimization problems and propose satisficing Thompson sampling-based parallel Bayesian optimization (STS-PBO) approaches, including synchronous and asynchronous versions. We shift the target from an optimal solution to a satisficing solution that is easier to learn. The rate-distortion theory is introduced to construct a loss function that balances the amount of information that needs to be learned with sub-optimality, and the Blahut-Arimoto algorithm is adopted to compute the target solution that reaches the minimum information rate under the distortion limit at each step. Both discounted and undiscounted Bayesian cumulative regret bounds are theoretically derived for the proposed STS-PBO approaches. The effectiveness of the proposed methods is demonstrated on a fast-charging design problem of Lithium-ion batteries. The results are accordant with theoretical analyses, and show that our STS-PBO methods outperform both sequential counterparts and parallel BO with traditional Thompson sampling in both synchronous and asynchronous settings.

We commonly encounter the problem of identifying an optimally weight adjusted version of the empirical distribution of observed data, adhering to predefined constraints on the weights. Such constraints often manifest as restrictions on the moments, tail behaviour, shapes, number of modes, etc., of the resulting weight adjusted empirical distribution. In this article, we substantially enhance the flexibility of such methodology by introducing a nonparametrically imbued distributional constraints on the weights, and developing a general framework leveraging the maximum entropy principle and tools from optimal transport. The key idea is to ensure that the maximum entropy weight adjusted empirical distribution of the observed data is close to a pre-specified probability distribution in terms of the optimal transport metric while allowing for subtle departures. The versatility of the framework is demonstrated in the context of three disparate applications where data re-weighting is warranted to satisfy side constraints on the optimization problem at the heart of the statistical task: namely, portfolio allocation, semi-parametric inference for complex surveys, and ensuring algorithmic fairness in machine learning algorithms.

The deep learning architecture associated with ChatGPT and related generative AI products is known as transformers. Initially applied to Natural Language Processing, transformers and the self-attention mechanism they exploit have gained widespread interest across the natural sciences. The goal of this pedagogical and informal review is to introduce transformers to scientists. The review includes the mathematics underlying the attention mechanism, a description of the original transformer architecture, and a section on applications to time series and imaging data in astronomy. We include a Frequently Asked Questions section for readers who are curious about generative AI or interested in getting started with transformers for their research problem.

Protein structure-based property prediction has emerged as a promising approach for various biological tasks, such as protein function prediction and sub-cellular location estimation. The existing methods highly rely on experimental protein structure data and fail in scenarios where these data are unavailable. Predicted protein structures from AI tools (e.g., AlphaFold2) were utilized as alternatives. However, we observed that current practices, which simply employ accurately predicted structures during inference, suffer from notable degradation in prediction accuracy. While similar phenomena have been extensively studied in general fields (e.g., Computer Vision) as model robustness, their impact on protein property prediction remains unexplored. In this paper, we first investigate the reason behind the performance decrease when utilizing predicted structures, attributing it to the structure embedding bias from the perspective of structure representation learning. To study this problem, we identify a Protein 3D Graph Structure Learning Problem for Robust Protein Property Prediction (PGSL-RP3), collect benchmark datasets, and present a protein Structure embedding Alignment Optimization framework (SAO) to mitigate the problem of structure embedding bias between the predicted and experimental protein structures. Extensive experiments have shown that our framework is model-agnostic and effective in improving the property prediction of both predicted structures and experimental structures. The benchmark datasets and codes will be released to benefit the community.

Training a large and state-of-the-art machine learning model typically necessitates the use of large-scale datasets, which, in turn, makes the training and parameter-tuning process expensive and time-consuming. Some researchers opt to distil information from real-world datasets into tiny and compact synthetic datasets while maintaining their ability to train a well-performing model, hence proposing a data-efficient method known as Dataset Distillation (DD). Despite recent progress in this field, existing methods still underperform and cannot effectively replace large datasets. In this paper, unlike previous methods that focus solely on improving the efficacy of student distillation, we are the first to recognize the important interplay between expert and student. We argue the significant impact of expert smoothness when employing more potent expert trajectories in subsequent dataset distillation. Based on this, we introduce the integration of clipping loss and gradient penalty to regulate the rate of parameter changes in expert trajectories. Furthermore, in response to the sensitivity exhibited towards randomly initialized variables during distillation, we propose representative initialization for synthetic dataset and balanced inner-loop loss. Finally, we present two enhancement strategies, namely intermediate matching loss and weight perturbation, to mitigate the potential occurrence of cumulative errors. We conduct extensive experiments on datasets of different scales, sizes, and resolutions. The results demonstrate that the proposed method significantly outperforms prior methods.

Feature preprocessing continues to play a critical role when applying machine learning and statistical methods to tabular data. In this paper, we propose the use of the kernel density integral transformation as a feature preprocessing step. Our approach subsumes the two leading feature preprocessing methods as limiting cases: linear min-max scaling and quantile transformation. We demonstrate that, without hyperparameter tuning, the kernel density integral transformation can be used as a simple drop-in replacement for either method, offering protection from the weaknesses of each. Alternatively, with tuning of a single continuous hyperparameter, we frequently outperform both of these methods. Finally, we show that the kernel density transformation can be profitably applied to statistical data analysis, particularly in correlation analysis and univariate clustering.

Narrow bit-width data formats are key to reducing the computational and storage costs of modern deep learning applications. This paper evaluates Microscaling (MX) data formats that combine a per-block scaling factor with narrow floating-point and integer types for individual elements. MX formats balance the competing needs of hardware efficiency, model accuracy, and user friction. Empirical results on over two dozen benchmarks demonstrate practicality of MX data formats as a drop-in replacement for baseline FP32 for AI inference and training with low user friction. We also show the first instance of training generative language models at sub-8-bit weights, activations, and gradients with minimal accuracy loss and no modifications to the training recipe.

The success of AI assistants based on language models (LLMs) hinges crucially on Reinforcement Learning from Human Feedback (RLHF), which enables the generation of responses more aligned with human preferences. As universal AI assistants, there's a growing expectation for them to perform consistently across various domains. However, previous work shows that Reinforcement Learning (RL) often exploits shortcuts to attain high rewards and overlooks challenging samples. This focus on quick reward gains undermines both the stability in training and the model's ability to generalize to new, unseen data. In this work, we propose a novel approach that can learn a consistent policy via RL across various data groups or domains. Given the challenges associated with acquiring group annotations, our method automatically classifies data into different groups, deliberately maximizing performance variance. Then, we optimize the policy to perform well on challenging groups. Lastly, leveraging the established groups, our approach adaptively adjusts the exploration space, allocating more learning capacity to more challenging data and preventing the model from over-optimizing on simpler data. Experimental results indicate that our approach significantly enhances training stability and model generalization.

Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting, where the goal is to model and forecast multivariate time-series that have underlying hierarchical relations. Most methods focus on point predictions and do not provide well-calibrated probabilistic forecasts distributions. Recent state-of-art probabilistic forecasting methods also impose hierarchical relations on point predictions and samples of distribution which does not account for coherency of forecast distributions. Previous works also silently assume that datasets are always consistent with given hierarchical relations and do not adapt to real-world datasets that show deviation from this assumption. We close both these gap and propose PROFHiT, which is a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy. PROFHiT uses a flexible probabilistic Bayesian approach and introduces a novel Distributional Coherency regularization to learn from hierarchical relations for entire forecast distribution that enables robust and calibrated forecasts as well as adapt to datasets of varying hierarchical consistency. On evaluating PROFHiT over wide range of datasets, we observed 41-88% better performance in accuracy and significantly better calibration. Due to modeling the coherency over full distribution, we observed that PROFHiT can robustly provide reliable forecasts even if up to 10% of input time-series data is missing where other methods' performance severely degrade by over 70%.

Data privacy and long-tailed distribution are the norms rather than the exception in many real-world tasks. This paper investigates a federated long-tailed learning (Fed-LT) task in which each client holds a locally heterogeneous dataset; if the datasets can be globally aggregated, they jointly exhibit a long-tailed distribution. Under such a setting, existing federated optimization and/or centralized long-tailed learning methods hardly apply due to challenges in (a) characterizing the global long-tailed distribution under privacy constraints and (b) adjusting the local learning strategy to cope with the head-tail imbalance. In response, we propose a method termed $\texttt{Fed-GraB}$, comprised of a Self-adjusting Gradient Balancer (SGB) module that re-weights clients' gradients in a closed-loop manner, based on the feedback of global long-tailed distribution evaluated by a Direct Prior Analyzer (DPA) module. Using $\texttt{Fed-GraB}$, clients can effectively alleviate the distribution drift caused by data heterogeneity during the model training process and obtain a global model with better performance on the minority classes while maintaining the performance of the majority classes. Extensive experiments demonstrate that $\texttt{Fed-GraB}$ achieves state-of-the-art performance on representative datasets such as CIFAR-10-LT, CIFAR-100-LT, ImageNet-LT, and iNaturalist.

Finding and selecting new and interesting problems to solve is at the heart of curiosity, science and innovation. We here study automated problem generation in the context of the open-ended space of python programming puzzles. Existing generative models often aim at modeling a reference distribution without any explicit diversity optimization. Other methods explicitly optimizing for diversity do so either in limited hand-coded representation spaces or in uninterpretable learned embedding spaces that may not align with human perceptions of interesting variations. With ACES (Autotelic Code Exploration via Semantic descriptors), we introduce a new autotelic generation method that leverages semantic descriptors produced by a large language model (LLM) to directly optimize for interesting diversity, as well as few-shot-based generation. Each puzzle is labeled along 10 dimensions, each capturing a programming skill required to solve it. ACES generates and pursues novel and feasible goals to explore that abstract semantic space, slowly discovering a diversity of solvable programming puzzles in any given run. Across a set of experiments, we show that ACES discovers a richer diversity of puzzles than existing diversity-maximizing algorithms as measured across a range of diversity metrics. We further study whether and in which conditions this diversity can translate into the successful training of puzzle solving models.

Representation learning has significantly driven the field to develop pretrained models that can act as a valuable starting point when transferring to new datasets. With the rising demand for reliable machine learning and uncertainty quantification, there is a need for pretrained models that not only provide embeddings but also transferable uncertainty estimates. To guide the development of such models, we propose the Uncertainty-aware Representation Learning (URL) benchmark. Besides the transferability of the representations, it also measures the zero-shot transferability of the uncertainty estimate using a novel metric. We apply URL to evaluate eleven uncertainty quantifiers that are pretrained on ImageNet and transferred to eight downstream datasets. We find that approaches that focus on the uncertainty of the representation itself or estimate the prediction risk directly outperform those that are based on the probabilities of upstream classes. Yet, achieving transferable uncertainty quantification remains an open challenge. Our findings indicate that it is not necessarily in conflict with traditional representation learning goals. Code is provided under https://github.com/mkirchhof/url .

In this article we present new results about the expressivity of Graph Neural Networks (GNNs). We prove that for any GNN with piecewise polynomial activations, whose architecture size does not grow with the graph input sizes, there exists a pair of non-isomorphic rooted trees of depth two such that the GNN cannot distinguish their root vertex up to an arbitrary number of iterations. The proof relies on tools from the algebra of symmetric polynomials. In contrast, it was already known that unbounded GNNs (those whose size is allowed to change with the graph sizes) with piecewise polynomial activations can distinguish these vertices in only two iterations. Our results imply a strict separation between bounded and unbounded size GNNs, answering an open question formulated by [Grohe, 2021]. We next prove that if one allows activations that are not piecewise polynomial, then in two iterations a single neuron perceptron can distinguish the root vertices of any pair of nonisomorphic trees of depth two (our results hold for activations like the sigmoid, hyperbolic tan and others). This shows how the power of graph neural networks can change drastically if one changes the activation function of the neural networks. The proof of this result utilizes the Lindemann-Weierstrauss theorem from transcendental number theory.

Large language models (LMs) are currently trained to predict tokens given document prefixes, enabling them to directly perform long-form generation and prompting-style tasks which can be reduced to document completion. Existing pretraining pipelines train LMs by concatenating random sets of short documents to create input contexts but the prior documents provide no signal for predicting the next document. We instead present In-Context Pretraining, a new approach where language models are pretrained on a sequence of related documents, thereby explicitly encouraging them to read and reason across document boundaries. We can do In-Context Pretraining by simply changing the document ordering so that each context contains related documents, and directly applying existing pretraining pipelines. However, this document sorting problem is challenging. There are billions of documents and we would like the sort to maximize contextual similarity for every document without repeating any data. To do this, we introduce approximate algorithms for finding related documents with efficient nearest neighbor search and constructing coherent input contexts with a graph traversal algorithm. Our experiments show In-Context Pretraining offers a simple and scalable approach to significantly enhance LMs'performance: we see notable improvements in tasks that require more complex contextual reasoning, including in-context learning (+8%), reading comprehension (+15%), faithfulness to previous contexts (+16%), long-context reasoning (+5%), and retrieval augmentation (+9%).

Generative self-supervised learning (SSL) has exhibited significant potential and garnered increasing interest in graph learning. In this study, we aim to explore the problem of generative SSL in the context of heterogeneous graph learning (HGL). The previous SSL approaches for heterogeneous graphs have primarily relied on contrastive learning, necessitating the design of complex views to capture heterogeneity. However, existing generative SSL methods have not fully leveraged the capabilities of generative models to address the challenges of HGL. In this paper, we present HGCVAE, a novel contrastive variational graph auto-encoder that liberates HGL from the burden of intricate heterogeneity capturing. Instead of focusing on complicated heterogeneity, HGCVAE harnesses the full potential of generative SSL. HGCVAE innovatively consolidates contrastive learning with generative SSL, introducing several key innovations. Firstly, we employ a progressive mechanism to generate high-quality hard negative samples for contrastive learning, utilizing the power of variational inference. Additionally, we present a dynamic mask strategy to ensure effective and stable learning. Moreover, we propose an enhanced scaled cosine error as the criterion for better attribute reconstruction. As an initial step in combining generative and contrastive SSL, HGCVAE achieves remarkable results compared to various state-of-the-art baselines, confirming its superiority.

Movement primitives are trainable parametric models that reproduce robotic movements starting from a limited set of demonstrations. Previous works proposed simple linear models that exhibited high sample efficiency and generalization power by allowing temporal modulation of movements (reproducing movements faster or slower), blending (merging two movements into one), via-point conditioning (constraining a movement to meet some particular via-points) and context conditioning (generation of movements based on an observed variable, e.g., position of an object). Previous works have proposed neural network-based motor primitive models, having demonstrated their capacity to perform tasks with some forms of input conditioning or time-modulation representations. However, there has not been a single unified deep motor primitive's model proposed that is capable of all previous operations, limiting neural motor primitive's potential applications. This paper proposes a deep movement primitive architecture that encodes all the operations above and uses a Bayesian context aggregator that allows a more sound context conditioning and blending. Our results demonstrate our approach can scale to reproduce complex motions on a larger variety of input choices compared to baselines while maintaining operations of linear movement primitives provide.

The mixing of two or more languages is called Code-Mixing (CM). CM is a social norm in multilingual societies. Neural Language Models (NLMs) like transformers have been effective on many NLP tasks. However, NLM for CM is an under-explored area. Though transformers are capable and powerful, they cannot always encode positional information since they are non-recurrent. Therefore, to enrich word information and incorporate positional information, positional encoding is defined. We hypothesize that Switching Points (SPs), i.e., junctions in the text where the language switches (L1 -> L2 or L2 -> L1), pose a challenge for CM Language Models (LMs), and hence give special emphasis to SPs in the modeling process. We experiment with several positional encoding mechanisms and show that rotatory positional encodings along with switching point information yield the best results. We introduce CONFLATOR: a neural language modeling approach for code-mixed languages. CONFLATOR tries to learn to emphasize switching points using smarter positional encoding, both at unigram and bigram levels. CONFLATOR outperforms the state-of-the-art on two tasks based on code-mixed Hindi and English (Hinglish): (i) sentiment analysis and (ii) machine translation.

If machine learning models were to achieve superhuman abilities at various reasoning or decision-making tasks, how would we go about evaluating such models, given that humans would necessarily be poor proxies for ground truth? In this paper, we propose a framework for evaluating superhuman models via consistency checks. Our premise is that while the correctness of superhuman decisions may be impossible to evaluate, we can still surface mistakes if the model's decisions fail to satisfy certain logical, human-interpretable rules. We instantiate our framework on three tasks where correctness of decisions is hard to evaluate due to either superhuman model abilities, or to otherwise missing ground truth: evaluating chess positions, forecasting future events, and making legal judgments. We show that regardless of a model's (possibly superhuman) performance on these tasks, we can discover logical inconsistencies in decision making. For example: a chess engine assigning opposing valuations to semantically identical boards; GPT-4 forecasting that sports records will evolve non-monotonically over time; or an AI judge assigning bail to a defendant only after we add a felony to their criminal record.

Neural networks have achieved remarkable empirical performance, while the current theoretical analysis is not adequate for understanding their success, e.g., the Neural Tangent Kernel approach fails to capture their key feature learning ability, while recent analyses on feature learning are typically problem-specific. This work proposes a unified analysis framework for two-layer networks trained by gradient descent. The framework is centered around the principle of feature learning from gradients, and its effectiveness is demonstrated by applications in several prototypical problems, such as mixtures of Gaussians and parity functions. The framework also sheds light on interesting network learning phenomena such as feature learning beyond kernels and the lottery ticket hypothesis.

Supervised contrastive loss (SCL) is a competitive and often superior alternative to the cross-entropy loss for classification. While prior studies have demonstrated that both losses yield symmetric training representations under balanced data, this symmetry breaks under class imbalances. This paper presents an intriguing discovery: the introduction of a ReLU activation at the final layer effectively restores the symmetry in SCL-learned representations. We arrive at this finding analytically, by establishing that the global minimizers of an unconstrained features model with SCL loss and entry-wise non-negativity constraints form an orthogonal frame. Extensive experiments conducted across various datasets, architectures, and imbalance scenarios corroborate our finding. Importantly, our experiments reveal that the inclusion of the ReLU activation restores symmetry without compromising test accuracy. This constitutes the first geometry characterization of SCL under imbalances. Additionally, our analysis and experiments underscore the pivotal role of batch selection strategies in representation geometry. By proving necessary and sufficient conditions for mini-batch choices that ensure invariant symmetric representations, we introduce batch-binding as an efficient strategy that guarantees these conditions hold.

Large Language Models (LLMs) have shown impressive performance as general purpose agents, but their abilities remain highly dependent on prompts which are hand written with onerous trial-and-error effort. We propose a simple and nonparametric solution to this problem, Automatic Prompt Optimization (APO), which is inspired by numerical gradient descent to automatically improve prompts, assuming access to training data and an LLM API. The algorithm uses minibatches of data to form natural language "gradients" that criticize the current prompt. The gradients are then "propagated" into the prompt by editing the prompt in the opposite semantic direction of the gradient. These gradient descent steps are guided by a beam search and bandit selection procedure which significantly improves algorithmic efficiency. Preliminary results across three benchmark NLP tasks and the novel problem of LLM jailbreak detection suggest that Automatic Prompt Optimization can outperform prior prompt editing techniques and improve an initial prompt's performance by up to 31%, by using data to rewrite vague task descriptions into more precise annotation instructions.

Diffusion models are generative models, which gradually add and remove noise to learn the underlying distribution of training data for data generation. The components of diffusion models have gained significant attention with many design choices proposed. Existing reviews have primarily focused on higher-level solutions, thereby covering less on the design fundamentals of components. This study seeks to address this gap by providing a comprehensive and coherent review on component-wise design choices in diffusion models. Specifically, we organize this review according to their three key components, namely the forward process, the reverse process, and the sampling procedure. This allows us to provide a fine-grained perspective of diffusion models, benefiting future studies in the analysis of individual components, the applicability of design choices, and the implementation of diffusion models.

Parameter-efficient fine-tuning (PEFT) of pre-trained language models (PLMs) has emerged as a highly successful approach, with training only a small number of parameters without sacrificing performance and becoming the de-facto learning paradigm with the increasing size of PLMs. However, existing PEFT methods are not memory-efficient, because they still require caching most of the intermediate activations for the gradient calculation, akin to fine-tuning. One effective way to reduce the activation memory is to apply a reversible model, so the intermediate activations are not necessary to be cached and can be recomputed. Nevertheless, modifying a PLM to its reversible variant is not straightforward, since the reversible model has a distinct architecture from the currently released PLMs. In this paper, we first investigate what is a key factor for the success of existing PEFT methods, and realize that it's essential to preserve the PLM's starting point when initializing a PEFT method. With this finding, we propose memory-efficient fine-tuning (MEFT) that inserts adapters into a PLM, preserving the PLM's starting point and making it reversible without additional pre-training. We evaluate MEFT on the GLUE benchmark and five question-answering tasks with various backbones, BERT, RoBERTa, BART and OPT. MEFT significantly reduces the activation memory up to 84% of full fine-tuning with a negligible amount of trainable parameters. Moreover, MEFT achieves the same score on GLUE and a comparable score on the question-answering tasks as full fine-tuning. A similar finding is also observed for the image classification task.

As generative AI becomes more prevalent, it is important to study how human users interact with such models. In this work, we investigate how people use text-to-image models to generate desired target images. To study this interaction, we created ArtWhisperer, an online game where users are given a target image and are tasked with iteratively finding a prompt that creates a similar-looking image as the target. Through this game, we recorded over 50,000 human-AI interactions; each interaction corresponds to one text prompt created by a user and the corresponding generated image. The majority of these are repeated interactions where a user iterates to find the best prompt for their target image, making this a unique sequential dataset for studying human-AI collaborations. In an initial analysis of this dataset, we identify several characteristics of prompt interactions and user strategies. People submit diverse prompts and are able to discover a variety of text descriptions that generate similar images. Interestingly, prompt diversity does not decrease as users find better prompts. We further propose a new metric to quantify the steerability of AI using our dataset. We define steerability as the expected number of interactions required to adequately complete a task. We estimate this value by fitting a Markov chain for each target task and calculating the expected time to reach an adequate score in the Markov chain. We quantify and compare AI steerability across different types of target images and two different models, finding that images of cities and natural world images are more steerable than artistic and fantasy images. These findings provide insights into human-AI interaction behavior, present a concrete method of assessing AI steerability, and demonstrate the general utility of the ArtWhisperer dataset.

The Quasi Manhattan Wasserstein Distance (QMWD) is a metric designed to quantify the dissimilarity between two matrices by combining elements of the Wasserstein Distance with specific transformations. It offers improved time and space complexity compared to the Manhattan Wasserstein Distance (MWD) while maintaining accuracy. QMWD is particularly advantageous for large datasets or situations with limited computational resources. This article provides a detailed explanation of QMWD, its computation, complexity analysis, and comparisons with WD and MWD.

This paper proposes the use of causal modeling to detect and mitigate algorithmic bias. We provide a brief description of causal modeling and a general overview of our approach. We then use the Adult dataset, which is available for download from the UC Irvine Machine Learning Repository, to develop (1) a prediction model, which is treated as a black box, and (2) a causal model for bias mitigation. In this paper, we focus on gender bias and the problem of binary classification. We show that gender bias in the prediction model is statistically significant at the 0.05 level. We demonstrate the effectiveness of the causal model in mitigating gender bias by cross-validation. Furthermore, we show that the overall classification accuracy is improved slightly. Our novel approach is intuitive, easy-to-use, and can be implemented using existing statistical software tools such as "lavaan" in R. Hence, it enhances explainability and promotes trust.

This paper studies a long-term resource allocation problem over multiple periods where each period requires a multi-stage decision-making process. We formulate the problem as an online allocation problem in an episodic finite-horizon constrained Markov decision process with an unknown non-stationary transition function and stochastic non-stationary reward and resource consumption functions. We propose the observe-then-decide regime and improve the existing decide-then-observe regime, while the two settings differ in how the observations and feedback about the reward and resource consumption functions are given to the decision-maker. We develop an online dual mirror descent algorithm that achieves near-optimal regret bounds for both settings. For the observe-then-decide regime, we prove that the expected regret against the dynamic clairvoyant optimal policy is bounded by $\tilde O(\rho^{-1}{H^{3/2}}S\sqrt{AT})$ where $\rho\in(0,1)$ is the budget parameter, $H$ is the length of the horizon, $S$ and $A$ are the numbers of states and actions, and $T$ is the number of episodes. For the decide-then-observe regime, we show that the regret against the static optimal policy that has access to the mean reward and mean resource consumption functions is bounded by $\tilde O(\rho^{-1}{H^{3/2}}S\sqrt{AT})$ with high probability. We test the numerical efficiency of our method for a variant of the resource-constrained inventory management problem.

Most existing parametric query optimization (PQO) techniques rely on traditional query optimizer cost models, which are often inaccurate and result in suboptimal query performance. We propose Kepler, an end-to-end learning-based approach to PQO that demonstrates significant speedups in query latency over a traditional query optimizer. Central to our method is Row Count Evolution (RCE), a novel plan generation algorithm based on perturbations in the sub-plan cardinality space. While previous approaches require accurate cost models, we bypass this requirement by evaluating candidate plans via actual execution data and training an ML model to predict the fastest plan given parameter binding values. Our models leverage recent advances in neural network uncertainty in order to robustly predict faster plans while avoiding regressions in query performance. Experimentally, we show that Kepler achieves significant improvements in query runtime on multiple datasets on PostgreSQL.

Multi-modal Contrastive Representation learning aims to encode different modalities into a semantically aligned shared space. This paradigm shows remarkable generalization ability on numerous downstream tasks across various modalities. However, the reliance on massive high-quality data pairs limits its further development on more modalities. This paper proposes a novel training-efficient method for learning MCR without paired data called Connecting Multi-modal Contrastive Representations (C-MCR). Specifically, given two existing MCRs pre-trained on (A, B) and (B, C) modality pairs, we project them to a new space and use the data from the overlapping modality B to aligning the two MCRs in the new space. Meanwhile, since the modality pairs (A, B) and (B, C) are already aligned within each MCR, the connection learned by overlapping modality can also be transferred to non-overlapping modality pair (A, C). To unleash the potential of C-MCR, we further introduce a semantic-enhanced inter- and intra-MCR connection method. We first enhance the semantic consistency and completion of embeddings across different modalities for more robust alignment. Then we utilize the inter-MCR alignment to establish the connection, and employ the intra-MCR alignment to better maintain the connection for inputs from non-overlapping modalities. To demonstrate the effectiveness of C-MCR, we connect CLIP and CLAP via texts to derive audio-visual representations, and integrate CLIP and ULIP via images for 3D-language representations. Remarkably, without using any paired data, C-MCR for audio-visual achieves state-of-the-art performance on audio-image retrieval, audio-visual source localization, and counterfactual audio-image recognition tasks. Furthermore, C-MCR for 3D-language also attains advanced zero-shot 3D point cloud classification accuracy on ModelNet40.

Multifunctional biological neural networks exploit multistability in order to perform multiple tasks without changing any network properties. Enabling artificial neural networks (ANNs) to obtain certain multistabilities in order to perform several tasks, where each task is related to a particular attractor in the network's state space, naturally has many benefits from a machine learning perspective. Given the association to multistability, in this paper we explore how the relationship between different attractors influences the ability of a reservoir computer (RC), which is a dynamical system in the form of an ANN, to achieve multifunctionality. We construct the `seeing double' problem to systematically study how a RC reconstructs a coexistence of attractors when there is an overlap between them. As the amount of overlap increases, we discover that for multifunctionality to occur, there is a critical dependence on a suitable choice of the spectral radius for the RC's internal network connections. A bifurcation analysis reveals how multifunctionality emerges and is destroyed as the RC enters a chaotic regime that can lead to chaotic itinerancy.

This paper introduces a new regularized version of the robust $\tau$-regression estimator for analyzing high-dimensional datasets subject to gross contamination in the response variables and covariates (explanatory variables). The resulting estimator, termed adaptive $\tau$-Lasso, is robust to outliers and high-leverage points. It also incorporates an adaptive $\ell_1$-norm penalty term, which enables the selection of relevant variables and reduces the bias associated with large true regression coefficients. More specifically, this adaptive $\ell_1$-norm penalty term assigns a weight to each regression coefficient. For a fixed number of predictors $p$, we show that the adaptive $\tau$-Lasso has the oracle property, ensuring both variable-selection consistency and asymptotic normality. Asymptotic normality applies only to the entries of the regression vector corresponding to the true support, assuming knowledge of the true regression vector support. We characterize its robustness via the finite-sample breakdown point and the influence function. We carry out extensive simulations and observe that the class of $\tau$-Lasso estimators exhibits robustness and reliable performance in both contaminated and uncontaminated data settings. We also validate our theoretical findings on robustness properties through simulation experiments. In the face of outliers and high-leverage points, the adaptive $\tau$-Lasso and $\tau$-Lasso estimators achieve the best performance or close-to-best performance in terms of prediction and variable selection accuracy compared to other competing regularized estimators for all scenarios considered in this study. Therefore, the adaptive $\tau$-Lasso and $\tau$-Lasso estimators can be effectively employed for a variety of sparse linear regression problems, particularly in high-dimensional settings and when the data is contaminated by outliers and high-leverage points.

Language-Conditioned Robotic Grasping (LCRG) aims to develop robots that ground and grasp objects based on natural language instructions. While robots capable of recognizing personal objects like "my wallet" can interact more naturally with non-expert users, current LCRG systems primarily limit robots to understanding only generic expressions. To this end, we introduce a task scenario GraspMine with a novel dataset that aims to locate and grasp personal objects given personal indicators via learning from a single human-robot interaction. To address GraspMine, we propose Personalized Grasping Agent (PGA), that learns personal objects by propagating user-given information through a Reminiscence-a collection of raw images from the user's environment. Specifically, PGA acquires personal object information by a user presenting a personal object with its associated indicator, followed by PGA inspecting the object by rotating it. Based on the acquired information, PGA pseudo-labels objects in the Reminiscence by our proposed label propagation algorithm. Harnessing the information acquired from the interactions and the pseudo-labeled objects in the Reminiscence, PGA adapts the object grounding model to grasp personal objects. Experiments on GraspMine show that PGA significantly outperforms baseline methods both in offline and online settings, signifying its effectiveness and personalization applicability on real-world scenarios. Finally, qualitative analysis shows the effectiveness of PGA through a detailed investigation of results in each phase.

We propose in this paper, STANLEY, a STochastic gradient ANisotropic LangEvin dYnamics, for sampling high dimensional data. With the growing efficacy and potential of Energy-Based modeling, also known as non-normalized probabilistic modeling, for modeling a generative process of different natures of high dimensional data observations, we present an end-to-end learning algorithm for Energy-Based models (EBM) with the purpose of improving the quality of the resulting sampled data points. While the unknown normalizing constant of EBMs makes the training procedure intractable, resorting to Markov Chain Monte Carlo (MCMC) is in general a viable option. Realizing what MCMC entails for the EBM training, we propose in this paper, a novel high dimensional sampling method, based on an anisotropic stepsize and a gradient-informed covariance matrix, embedded into a discretized Langevin diffusion. We motivate the necessity for an anisotropic update of the negative samples in the Markov Chain by the nonlinearity of the backbone of the EBM, here a Convolutional Neural Network. Our resulting method, namely STANLEY, is an optimization algorithm for training Energy-Based models via our newly introduced MCMC method. We provide a theoretical understanding of our sampling scheme by proving that the sampler leads to a geometrically uniformly ergodic Markov Chain. Several image generation experiments are provided in our paper to show the effectiveness of our method.

Privacy estimation techniques for differentially private (DP) algorithms are useful for comparing against analytical bounds, or to empirically measure privacy loss in settings where known analytical bounds are not tight. However, existing privacy auditing techniques usually make strong assumptions on the adversary (e.g., knowledge of intermediate model iterates or the training data distribution), are tailored to specific tasks, model architectures, or DP algorithm, and/or require retraining the model many times (typically on the order of thousands). These shortcomings make deploying such techniques at scale difficult in practice, especially in federated settings where model training can take days or weeks. In this work, we present a novel ``one-shot'' approach that can systematically address these challenges, allowing efficient auditing or estimation of the privacy loss of a model during the same, single training run used to fit model parameters, and without requiring any a priori knowledge about the model architecture, task, or DP training algorithm. We show that our method provides provably correct estimates for the privacy loss under the Gaussian mechanism, and we demonstrate its performance on well-established FL benchmark datasets under several adversarial threat models.

A recommender system that optimizes its recommendations solely to fit a user's history of ratings for consumed items can create a filter bubble, wherein the user does not get to experience items from novel, unseen categories. One approach to mitigate this undesired behavior is to recommend items with high potential for serendipity, namely surprising items that are likely to be highly rated. In this paper, we propose a content-based formulation of serendipity that is rooted in Bayesian surprise and use it to measure the serendipity of items after they are consumed and rated by the user. When coupled with a collaborative-filtering component that identifies similar users, this enables recommending items with high potential for serendipity. To facilitate the evaluation of topic-level models for surprise and serendipity, we introduce a dataset of book reading histories extracted from Goodreads, containing over 26 thousand users and close to 1.3 million books, where we manually annotate 449 books read by 4 users in terms of their time-dependent, topic-level surprise. Experimental evaluations show that models that use Bayesian surprise correlate much better with the manual annotations of topic-level surprise than distance-based heuristics, and also obtain better serendipitous item recommendation performance.

We report a flexible language-model based deep learning strategy, applied here to solve complex forward and inverse problems in protein modeling, based on an attention neural network that integrates transformer and graph convolutional architectures in a causal multi-headed graph mechanism, to realize a generative pretrained model. The model is applied to predict secondary structure content (per-residue level and overall content), protein solubility, and sequencing tasks. Further trained on inverse tasks, the model is rendered capable of designing proteins with these properties as target features. The model is formulated as a general framework, completely prompt-based, and can be adapted for a variety of downstream tasks. We find that adding additional tasks yields emergent synergies that the model exploits in improving overall performance, beyond what would be possible by training a model on each dataset alone. Case studies are presented to validate the method, yielding protein designs specifically focused on structural proteins, but also exploring the applicability in the design of soluble, antimicrobial biomaterials. While our model is trained to ultimately perform 8 distinct tasks, with available datasets it can be extended to solve additional problems. In a broader sense, this work illustrates a form of multiscale modeling that relates a set of ultimate building blocks (here, byte-level utf8 characters that define the nature of the physical system at hand) to complex output. This materiomic scheme captures complex emergent relationships between universal building block and resulting properties via a synergizing learning capacity to express a set of potentialities embedded in the knowledge used in training, via the interplay of universality and diversity.

Web-scale search systems learn an encoder to embed a given query which is then hooked into an approximate nearest neighbor search (ANNS) pipeline to retrieve similar data points. To accurately capture tail queries and data points, learned representations typically are rigid, high-dimensional vectors that are generally used as-is in the entire ANNS pipeline and can lead to computationally expensive retrieval. In this paper, we argue that instead of rigid representations, different stages of ANNS can leverage adaptive representations of varying capacities to achieve significantly better accuracy-compute trade-offs, i.e., stages of ANNS that can get away with more approximate computation should use a lower-capacity representation of the same data point. To this end, we introduce AdANNS, a novel ANNS design framework that explicitly leverages the flexibility of Matryoshka Representations. We demonstrate state-of-the-art accuracy-compute trade-offs using novel AdANNS-based key ANNS building blocks like search data structures (AdANNS-IVF) and quantization (AdANNS-OPQ). For example on ImageNet retrieval, AdANNS-IVF is up to 1.5% more accurate than the rigid representations-based IVF at the same compute budget; and matches accuracy while being up to 90x faster in wall-clock time. For Natural Questions, 32-byte AdANNS-OPQ matches the accuracy of the 64-byte OPQ baseline constructed using rigid representations -- same accuracy at half the cost! We further show that the gains from AdANNS translate to modern-day composite ANNS indices that combine search structures and quantization. Finally, we demonstrate that AdANNS can enable inference-time adaptivity for compute-aware search on ANNS indices built non-adaptively on matryoshka representations. Code is open-sourced at https://github.com/RAIVNLab/AdANNS.

This work introduces the first toolkit around path-norms that is fully able to encompass general DAG ReLU networks with biases, skip connections and any operation based on the extraction of order statistics: max pooling, GroupSort etc. This toolkit notably allows us to establish generalization bounds for modern neural networks that are not only the most widely applicable path-norm based ones, but also recover or beat the sharpest known bounds of this type. These extended path-norms further enjoy the usual benefits of path-norms: ease of computation, invariance under the symmetries of the network, and improved sharpness on feedforward networks compared to the product of operators' norms, another complexity measure most commonly used. The versatility of the toolkit and its ease of implementation allow us to challenge the concrete promises of path-norm-based generalization bounds, by numerically evaluating the sharpest known bounds for ResNets on ImageNet.

The training process of ReLU neural networks often exhibits complicated nonlinear phenomena. The nonlinearity of models and non-convexity of loss pose significant challenges for theoretical analysis. Therefore, most previous theoretical works on the optimization dynamics of neural networks focus either on local analysis (like the end of training) or approximate linear models (like Neural Tangent Kernel). In this work, we conduct a complete theoretical characterization of the training process of a two-layer ReLU network trained by Gradient Flow on a linearly separable data. In this specific setting, our analysis captures the whole optimization process starting from random initialization to final convergence. Despite the relatively simple model and data that we studied, we reveal four different phases from the whole training process showing a general simplifying-to-complicating learning trend. Specific nonlinear behaviors can also be precisely identified and captured theoretically, such as initial condensation, saddle-to-plateau dynamics, plateau escape, changes of activation patterns, learning with increasing complexity, etc.

The transformer is a neural network component that can be used to learn useful representations of sequences or sets of data-points. The transformer has driven recent advances in natural language processing, computer vision, and spatio-temporal modelling. There are many introductions to transformers, but most do not contain precise mathematical descriptions of the architecture and the intuitions behind the design choices are often also missing. Moreover, as research takes a winding path, the explanations for the components of the transformer can be idiosyncratic. In this note we aim for a mathematically precise, intuitive, and clean description of the transformer architecture. We will not discuss training as this is rather standard. We assume that the reader is familiar with fundamental topics in machine learning including multi-layer perceptrons, linear transformations, softmax functions and basic probability.

Self-supervised Learning (SSL) including the mainstream contrastive learning has achieved great success in learning visual representations without data annotations. However, most methods mainly focus on the instance level information (\ie, the different augmented images of the same instance should have the same feature or cluster into the same class), but there is a lack of attention on the relationships between different instances. In this paper, we introduce a novel SSL paradigm, which we term as relational self-supervised learning (ReSSL) framework that learns representations by modeling the relationship between different instances. Specifically, our proposed method employs sharpened distribution of pairwise similarities among different instances as \textit{relation} metric, which is thus utilized to match the feature embeddings of different augmentations. To boost the performance, we argue that weak augmentations matter to represent a more reliable relation, and leverage momentum strategy for practical efficiency. The designed asymmetric predictor head and an InfoNCE warm-up strategy enhance the robustness to hyper-parameters and benefit the resulting performance. Experimental results show that our proposed ReSSL substantially outperforms the state-of-the-art methods across different network architectures, including various lightweight networks (\eg, EfficientNet and MobileNet).

Embodied agents operate in a structured world, often solving tasks with spatial, temporal, and permutation symmetries. Most algorithms for planning and model-based reinforcement learning (MBRL) do not take this rich geometric structure into account, leading to sample inefficiency and poor generalization. We introduce the Equivariant Diffuser for Generating Interactions (EDGI), an algorithm for MBRL and planning that is equivariant with respect to the product of the spatial symmetry group SE(3), the discrete-time translation group Z, and the object permutation group Sn. EDGI follows the Diffuser framework (Janner et al., 2022) in treating both learning a world model and planning in it as a conditional generative modeling problem, training a diffusion model on an offline trajectory dataset. We introduce a new SE(3)xZxSn-equivariant diffusion model that supports multiple representations. We integrate this model in a planning loop, where conditioning and classifier guidance let us softly break the symmetry for specific tasks as needed. On object manipulation and navigation tasks, EDGI is substantially more sample efficient and generalizes better across the symmetry group than non-equivariant models.

Data Augmentation (DA) has emerged as an indispensable strategy in Time Series Classification (TSC), primarily due to its capacity to amplify training samples, thereby bolstering model robustness, diversifying datasets, and curtailing overfitting. However, the current landscape of DA in TSC is plagued with fragmented literature reviews, nebulous methodological taxonomies, inadequate evaluative measures, and a dearth of accessible, user-oriented tools. In light of these challenges, this study embarks on an exhaustive dissection of DA methodologies within the TSC realm. Our initial approach involved an extensive literature review spanning a decade, revealing that contemporary surveys scarcely capture the breadth of advancements in DA for TSC, prompting us to meticulously analyze over 100 scholarly articles to distill more than 60 unique DA techniques. This rigorous analysis precipitated the formulation of a novel taxonomy, purpose-built for the intricacies of DA in TSC, categorizing techniques into five principal echelons: Transformation-Based, Pattern-Based, Generative, Decomposition-Based, and Automated Data Augmentation. Our taxonomy promises to serve as a robust navigational aid for scholars, offering clarity and direction in method selection. Addressing the conspicuous absence of holistic evaluations for prevalent DA techniques, we executed an all-encompassing empirical assessment, wherein upwards of 15 DA strategies were subjected to scrutiny across 8 UCR time-series datasets, employing ResNet and a multi-faceted evaluation paradigm encompassing Accuracy, Method Ranking, and Residual Analysis, yielding a benchmark accuracy of 88.94 +- 11.83%. Our investigation underscored the inconsistent efficacies of DA techniques, with...

Brain Signals, such as Electroencephalography (EEG), and human languages have been widely explored independently for many downstream tasks, however, the connection between them has not been well explored. In this study, we explore the relationship and dependency between EEG and language. To study at the representation level, we introduced \textbf{MTAM}, a \textbf{M}ultimodal \textbf{T}ransformer \textbf{A}lignment \textbf{M}odel, to observe coordinated representations between the two modalities. We used various relationship alignment-seeking techniques, such as Canonical Correlation Analysis and Wasserstein Distance, as loss functions to transfigure features. On downstream applications, sentiment analysis and relation detection, we achieved new state-of-the-art results on two datasets, ZuCo and K-EmoCon. Our method achieved an F1-score improvement of 1.7% on K-EmoCon and 9.3% on Zuco datasets for sentiment analysis, and 7.4% on ZuCo for relation detection. In addition, we provide interpretations of the performance improvement: (1) feature distribution shows the effectiveness of the alignment module for discovering and encoding the relationship between EEG and language; (2) alignment weights show the influence of different language semantics as well as EEG frequency features; (3) brain topographical maps provide an intuitive demonstration of the connectivity in the brain regions. Our code is available at \url{https://github.com/Jason-Qiu/EEG_Language_Alignment}.

Whether class labels in a given data set correspond to meaningful clusters is crucial for the evaluation of clustering algorithms using real-world data sets. This property can be quantified by separability measures. A review of the existing literature shows that neither classification-based complexity measures nor cluster validity indices (CVIs) adequately incorporate the central aspects of separability for density-based clustering: between-class separation and within-class connectedness. A newly developed measure (density cluster separability index, DCSI) aims to quantify these two characteristics and can also be used as a CVI. Extensive experiments on synthetic data indicate that DCSI correlates strongly with the performance of DBSCAN measured via the adjusted rand index (ARI) but lacks robustness when it comes to multi-class data sets with overlapping classes that are ill-suited for density-based hard clustering. Detailed evaluation on frequently used real-world data sets shows that DCSI can correctly identify touching or overlapping classes that do not form meaningful clusters.

Realistic and controllable traffic simulation is a core capability that is necessary to accelerate autonomous vehicle (AV) development. However, current approaches for controlling learning-based traffic models require significant domain expertise and are difficult for practitioners to use. To remedy this, we present CTG++, a scene-level conditional diffusion model that can be guided by language instructions. Developing this requires tackling two challenges: the need for a realistic and controllable traffic model backbone, and an effective method to interface with a traffic model using language. To address these challenges, we first propose a scene-level diffusion model equipped with a spatio-temporal transformer backbone, which generates realistic and controllable traffic. We then harness a large language model (LLM) to convert a user's query into a loss function, guiding the diffusion model towards query-compliant generation. Through comprehensive evaluation, we demonstrate the effectiveness of our proposed method in generating realistic, query-compliant traffic simulations.

Large-scale generative models such as GPT and DALL-E have revolutionized the research community. These models not only generate high fidelity outputs, but are also generalists which can solve tasks not explicitly taught. In contrast, speech generative models are still primitive in terms of scale and task generalization. In this paper, we present Voicebox, the most versatile text-guided generative model for speech at scale. Voicebox is a non-autoregressive flow-matching model trained to infill speech, given audio context and text, trained on over 50K hours of speech that are not filtered or enhanced. Similar to GPT, Voicebox can perform many different tasks through in-context learning, but is more flexible as it can also condition on future context. Voicebox can be used for mono or cross-lingual zero-shot text-to-speech synthesis, noise removal, content editing, style conversion, and diverse sample generation. In particular, Voicebox outperforms the state-of-the-art zero-shot TTS model VALL-E on both intelligibility (5.9% vs 1.9% word error rates) and audio similarity (0.580 vs 0.681) while being up to 20 times faster. Audio samples can be found in \url{https://voicebox.metademolab.com}.

Data-driven black-box model-based optimization (MBO) problems arise in a great number of practical application scenarios, where the goal is to find a design over the whole space maximizing a black-box target function based on a static offline dataset. In this work, we consider a more general but challenging MBO setting, named constrained MBO (CoMBO), where only part of the design space can be optimized while the rest is constrained by the environment. A new challenge arising from CoMBO is that most observed designs that satisfy the constraints are mediocre in evaluation. Therefore, we focus on optimizing these mediocre designs in the offline dataset while maintaining the given constraints rather than further boosting the best observed design in the traditional MBO setting. We propose retrieval-enhanced offline model-based optimization (ROMO), a new derivable forward approach that retrieves the offline dataset and aggregates relevant samples to provide a trusted prediction, and use it for gradient-based optimization. ROMO is simple to implement and outperforms state-of-the-art approaches in the CoMBO setting. Empirically, we conduct experiments on a synthetic Hartmann (3D) function dataset, an industrial CIO dataset, and a suite of modified tasks in the Design-Bench benchmark. Results show that ROMO performs well in a wide range of constrained optimization tasks.

Binary Neural Networks (BNNs) have emerged as a promising solution for reducing the memory footprint and compute costs of deep neural networks, but they suffer from quality degradation due to the lack of freedom as activations and weights are constrained to the binary values. To compensate for the accuracy drop, we propose a novel BNN design called Binary Neural Network with INSTAnce-aware threshold (INSTA-BNN), which controls the quantization threshold dynamically in an input-dependent or instance-aware manner. According to our observation, higher-order statistics can be a representative metric to estimate the characteristics of the input distribution. INSTA-BNN is designed to adjust the threshold dynamically considering various information, including higher-order statistics, but it is also optimized judiciously to realize minimal overhead on a real device. Our extensive study shows that INSTA-BNN outperforms the baseline by 3.0% and 2.8% on the ImageNet classification task with comparable computing cost, achieving 68.5% and 72.2% top-1 accuracy on ResNet-18 and MobileNetV1 based models, respectively.

Spectral clustering and its extensions usually consist of two steps: (1) constructing a graph and computing the relaxed solution; (2) discretizing relaxed solutions. Although the former has been extensively investigated, the discretization techniques are mainly heuristic methods, e.g., k-means, spectral rotation. Unfortunately, the goal of the existing methods is not to find a discrete solution that minimizes the original objective. In other words, the primary drawback is the neglect of the original objective when computing the discrete solution. Inspired by the first-order optimization algorithms, we propose to develop a first-order term to bridge the original problem and discretization algorithm, which is the first non-heuristic to the best of our knowledge. Since the non-heuristic method is aware of the original graph cut problem, the final discrete solution is more reliable and achieves the preferable loss value. We also theoretically show that the continuous optimum is beneficial to discretization algorithms though simply finding its closest discrete solution is an existing heuristic algorithm which is also unreliable. Sufficient experiments significantly show the superiority of our method.

Streaming submodular maximization is a natural model for the task of selecting a representative subset from a large-scale dataset. If datapoints have sensitive attributes such as gender or race, it becomes important to enforce fairness to avoid bias and discrimination. This has spurred significant interest in developing fair machine learning algorithms. Recently, such algorithms have been developed for monotone submodular maximization under a cardinality constraint. In this paper, we study the natural generalization of this problem to a matroid constraint. We give streaming algorithms as well as impossibility results that provide trade-offs between efficiency, quality and fairness. We validate our findings empirically on a range of well-known real-world applications: exemplar-based clustering, movie recommendation, and maximum coverage in social networks.

Recent parameter-efficient finetuning (PEFT) techniques aim to improve over the considerable cost of fully finetuning large pretrained language models (PLM). As different PEFT techniques proliferate, it is becoming difficult to compare them, in particular in terms of (i) the structure and functionality they add to the PLM, (ii) the different types and degrees of efficiency improvements achieved, (iii) performance at different downstream tasks, and (iv) how differences in structure and functionality relate to efficiency and task performance. To facilitate such comparisons, this paper presents a reference architecture which standardises aspects shared by different PEFT techniques, while isolating differences to specific locations and interactions with the standard components. Through this process of standardising and isolating differences, a modular view of PEFT techniques emerges, supporting not only direct comparison of different techniques and their efficiency and task performance, but also systematic exploration of reusability and composability of the different types of finetuned modules. We demonstrate how the reference architecture can be applied to understand properties and relative advantages of PEFT techniques, hence to inform selection of techniques for specific tasks, and design choices for new PEFT techniques.

Solving complex planning problems has been a long-standing challenge in computer science. Learning-based subgoal search methods have shown promise in tackling these problems, but they often suffer from a lack of completeness guarantees, meaning that they may fail to find a solution even if one exists. In this paper, we propose an efficient approach to augment a subgoal search method to achieve completeness in discrete action spaces. Specifically, we augment the high-level search with low-level actions to execute a multi-level (hybrid) search, which we call complete subgoal search. This solution achieves the best of both worlds: the practical efficiency of high-level search and the completeness of low-level search. We apply the proposed search method to a recently proposed subgoal search algorithm and evaluate the algorithm trained on offline data on complex planning problems. We demonstrate that our complete subgoal search not only guarantees completeness but can even improve performance in terms of search expansions for instances that the high-level could solve without low-level augmentations. Our approach makes it possible to apply subgoal-level planning for systems where completeness is a critical requirement.

Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting, where the goal is to model and forecast multivariate time-series that have underlying hierarchical relations. Most methods focus on point predictions and do not provide well-calibrated probabilistic forecasts distributions. Recent state-of-art probabilistic forecasting methods also impose hierarchical relations on point predictions and samples of distribution which does not account for coherency of forecast distributions. Previous works also silently assume that datasets are always consistent with given hierarchical relations and do not adapt to real-world datasets that show deviation from this assumption. We close both these gap and propose PROFHiT, which is a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy. PROFHiT uses a flexible probabilistic Bayesian approach and introduces a novel Distributional Coherency regularization to learn from hierarchical relations for entire forecast distribution that enables robust and calibrated forecasts as well as adapt to datasets of varying hierarchical consistency. On evaluating PROFHiT over wide range of datasets, we observed 41-88% better performance in accuracy and significantly better calibration. Due to modeling the coherency over full distribution, we observed that PROFHiT can robustly provide reliable forecasts even if up to 10% of input time-series data is missing where other methods' performance severely degrade by over 70%.

Prompt-based learning has been an effective paradigm for large pretrained language models (LLM), enabling few-shot or even zero-shot learning. Black-box prompt search has received growing interest recently for its distinctive properties of gradient-free optimization, proven particularly useful and powerful for model-as-a-service usage. However, the discrete nature and the complexity of combinatorial optimization hinder the efficiency of modern black-box approaches. Despite extensive research on search algorithms, the crucial aspect of search space design and optimization has been largely overlooked. In this paper, we first conduct a sensitivity analysis by prompting LLM, revealing that only a small number of tokens exert a disproportionate amount of influence on LLM predictions. Leveraging this insight, we propose the Clustering and Pruning for Efficient Black-box Prompt Search (ClaPS), a simple black-box search method that first clusters and prunes the search space to focus exclusively on influential prompt tokens. By employing even simple search methods within the pruned search space, ClaPS achieves state-of-the-art performance across various tasks and LLMs, surpassing the performance of complex approaches while significantly reducing search costs. Our findings underscore the critical role of search space design and optimization in enhancing both the usefulness and the efficiency of black-box prompt-based learning.

Urban environments are intricate systems where the breakdown of critical infrastructure can impact both the economic and social well-being of communities. Electricity systems hold particular significance, as they are essential for other infrastructure, and disruptions can trigger widespread consequences. Typically, assessing electricity availability requires ground-level data, a challenge in conflict zones and regions with limited access. This study shows how satellite imagery, social media, and information extraction can monitor blackouts and their perceived causes. Night-time light data (in March 2019 for Caracas, Venezuela) is used to indicate blackout regions. Twitter data is used to determine sentiment and topic trends, while statistical analysis and topic modeling delved into public perceptions regarding blackout causes. The findings show an inverse relationship between nighttime light intensity. Tweets mentioning the Venezuelan President displayed heightened negativity and a greater prevalence of blame-related terms, suggesting a perception of government accountability for the outages.

The reliance of text classifiers on spurious correlations can lead to poor generalization at deployment, raising concerns about their use in safety-critical domains such as healthcare. In this work, we propose to use counterfactual data augmentation, guided by knowledge of the causal structure of the data, to simulate interventions on spurious features and to learn more robust text classifiers. We show that this strategy is appropriate in prediction problems where the label is spuriously correlated with an attribute. Under the assumptions of such problems, we discuss the favorable sample complexity of counterfactual data augmentation, compared to importance re-weighting. Pragmatically, we match examples using auxiliary data, based on diff-in-diff methodology, and use a large language model (LLM) to represent a conditional probability of text. Through extensive experimentation on learning caregiver-invariant predictors of clinical diagnoses from medical narratives and on semi-synthetic data, we demonstrate that our method for simulating interventions improves out-of-distribution (OOD) accuracy compared to baseline invariant learning algorithms.

In this paper, we explore audio-editing with non-rigid text edits. We show that the proposed editing pipeline is able to create audio edits that remain faithful to the input audio. We explore text prompts that perform addition, style transfer, and in-painting. We quantitatively and qualitatively show that the edits are able to obtain results which outperform Audio-LDM, a recently released text-prompted audio generation model. Qualitative inspection of the results points out that the edits given by our approach remain more faithful to the input audio in terms of keeping the original onsets and offsets of the audio events.

Diagnosing and cleaning data is a crucial step for building robust machine learning systems. However, identifying problems within large-scale datasets with real-world distributions is challenging due to the presence of complex issues such as label errors, under-representation, and outliers. In this paper, we propose a unified approach for identifying the problematic data by utilizing a largely ignored source of information: a relational structure of data in the feature-embedded space. To this end, we present scalable and effective algorithms for detecting label errors and outlier data based on the relational graph structure of data. We further introduce a visualization tool that provides contextual information of a data point in the feature-embedded space, serving as an effective tool for interactively diagnosing data. We evaluate the label error and outlier/out-of-distribution (OOD) detection performances of our approach on the large-scale image, speech, and language domain tasks, including ImageNet, ESC-50, and SST2. Our approach achieves state-of-the-art detection performance on all tasks considered and demonstrates its effectiveness in debugging large-scale real-world datasets across various domains. We release codes at https://github.com/snu-mllab/Neural-Relation-Graph.

The quality of explanations for the predictions of complex machine learning predictors is often measured using insertion and deletion metrics, which assess the faithfulness of the explanations, i.e., how correctly the explanations reflect the predictor's behavior. To improve the faithfulness, we propose insertion/deletion metric-aware explanation-based optimization (ID-ExpO), which optimizes differentiable predictors to improve both insertion and deletion scores of the explanations while keeping their predictive accuracy. Since the original insertion and deletion metrics are indifferentiable with respect to the explanations and directly unavailable for gradient-based optimization, we extend the metrics to be differentiable and use them to formalize insertion and deletion metric-based regularizers. The experimental results on image and tabular datasets show that the deep neural networks-based predictors fine-tuned using ID-ExpO enable popular post-hoc explainers to produce more faithful and easy-to-interpret explanations while keeping high predictive accuracy.

As Natural Language Processing (NLP) algorithms continually achieve new milestones, out-of-distribution generalization remains a significant challenge. This paper addresses the issue of multi-source adaptation for unfamiliar domains: We leverage labeled data from multiple source domains to generalize to unknown target domains at training. Our innovative framework employs example-based Hypernetwork adaptation: a T5 encoder-decoder initially generates a unique signature from an input example, embedding it within the source domains' semantic space. This signature is subsequently utilized by a Hypernetwork to generate the task classifier's weights. We evaluated our method across two tasks - sentiment classification and natural language inference - in 29 adaptation scenarios, where it outpaced established algorithms. In an advanced version, the signature also enriches the input example's representation. We also compare our finetuned architecture to few-shot GPT-3, demonstrating its effectiveness in essential use cases. To our knowledge, this marks the first application of Hypernetworks to the adaptation for unknown domains.

Recent advancements in large language models (LLMs) have demonstrated remarkable abilities in handling a variety of natural language processing (NLP) downstream tasks, even on mathematical tasks requiring multi-step reasoning. In this report, we introduce the KwaiYiiMath which enhances the mathematical reasoning abilities of KwaiYiiBase1, by applying Supervised Fine-Tuning (SFT) and Reinforced Learning from Human Feedback (RLHF), including on both English and Chinese mathematical tasks. Meanwhile, we also constructed a small-scale Chinese primary school mathematics test set (named KMath), consisting of 188 examples to evaluate the correctness of the problem-solving process generated by the models. Empirical studies demonstrate that KwaiYiiMath can achieve state-of-the-art (SOTA) performance on GSM8k, CMath, and KMath compared with the similar size models, respectively.

We study the global linear convergence of policy gradient (PG) methods for finite-horizon continuous-time exploratory linear-quadratic control (LQC) problems. The setting includes stochastic LQC problems with indefinite costs and allows additional entropy regularisers in the objective. We consider a continuous-time Gaussian policy whose mean is linear in the state variable and whose covariance is state-independent. Contrary to discrete-time problems, the cost is noncoercive in the policy and not all descent directions lead to bounded iterates. We propose geometry-aware gradient descents for the mean and covariance of the policy using the Fisher geometry and the Bures-Wasserstein geometry, respectively. The policy iterates are shown to satisfy an a-priori bound, and converge globally to the optimal policy with a linear rate. We further propose a novel PG method with discrete-time policies. The algorithm leverages the continuous-time analysis, and achieves a robust linear convergence across different action frequencies. A numerical experiment confirms the convergence and robustness of the proposed algorithm.

If you ask a human to describe an image, they might do so in a thousand different ways. Traditionally, image captioning models are trained to generate a single "best" (most like a reference) image caption. Unfortunately, doing so encourages captions that are "informationally impoverished," and focus on only a subset of the possible details, while ignoring other potentially useful information in the scene. In this work, we introduce a simple, yet novel, method: "Image Captioning by Committee Consensus" (IC3), designed to generate a single caption that captures high-level details from several annotator viewpoints. Humans rate captions produced by IC3 at least as helpful as baseline SOTA models more than two thirds of the time, and IC3 can improve the performance of SOTA automated recall systems by up to 84%, outperforming single human-generated reference captions, and indicating significant improvements over SOTA approaches for visual description. Code is available at https://davidmchan.github.io/caption-by-committee/

We study the compute-optimal trade-off between model and training data set sizes for large neural networks. Our result suggests a linear relation similar to that supported by the empirical analysis of chinchilla. While that work studies transformer-based large language models trained on the MassiveText corpus gopher, as a starting point for development of a mathematical theory, we focus on a simpler learning model and data generating process, each based on a neural network with a sigmoidal output unit and single hidden layer of ReLU activation units. We introduce general error upper bounds for a class of algorithms which incrementally update a statistic (for example gradient descent). For a particular learning model inspired by barron 1993, we establish an upper bound on the minimal information-theoretically achievable expected error as a function of model and data set sizes. We then derive allocations of computation that minimize this bound. We present empirical results which suggest that this approximation correctly identifies an asymptotic linear compute-optimal scaling. This approximation also generates new insights. Among other things, it suggests that, as the input dimension or latent space complexity grows, as might be the case for example if a longer history of tokens is taken as input to a language model, a larger fraction of the compute budget should be allocated to growing the learning model rather than training data.

We introduce DISSC, a novel, lightweight method that converts the rhythm, pitch contour and timbre of a recording to a target speaker in a textless manner. Unlike DISSC, most voice conversion (VC) methods focus primarily on timbre, and ignore people's unique speaking style (prosody). The proposed approach uses a pretrained, self-supervised model for encoding speech to discrete units, which makes it simple, effective, and fast to train. All conversion modules are only trained on reconstruction like tasks, thus suitable for any-to-many VC with no paired data. We introduce a suite of quantitative and qualitative evaluation metrics for this setup, and empirically demonstrate that DISSC significantly outperforms the evaluated baselines. Code and samples are available at https://pages.cs.huji.ac.il/adiyoss-lab/dissc/.

An important application scenario of precision agriculture is detecting and measuring crop health threats using sensors and data analysis techniques. However, the textual data are still under-explored among the existing solutions due to the lack of labelled data and fine-grained semantic resources. Recent research suggests that the increasing connectivity of farmers and the emergence of online farming communities make social media like Twitter a participatory platform for detecting unfamiliar plant health events if we can extract essential information from unstructured textual data. ChouBERT is a French pre-trained language model that can identify Tweets concerning observations of plant health issues with generalizability on unseen natural hazards. This paper tackles the lack of labelled data by further studying ChouBERT's know-how on token-level annotation tasks over small labeled sets.

In recent years, reinforcement learning and bandits have transformed a wide range of real-world applications including healthcare, finance, recommendation systems, robotics, and last but not least, the speech and natural language processing. While most speech and language applications of reinforcement learning algorithms are centered around improving the training of deep neural networks with its flexible optimization properties, there are still many grounds to explore to utilize the benefits of reinforcement learning, such as its reward-driven adaptability, state representations, temporal structures and generalizability. In this survey, we present an overview of recent advancements of reinforcement learning and bandits, and discuss how they can be effectively employed to solve speech and natural language processing problems with models that are adaptive, interactive and scalable.

Deep discriminative approaches like random forests and deep neural networks have recently found applications in many important real-world scenarios. However, deploying these learning algorithms in safety-critical applications raises concerns, particularly when it comes to ensuring confidence calibration for both in-distribution and out-of-distribution data points. Many popular methods for in-distribution (ID) calibration, such as isotonic regression and Platt's sigmoidal regression, exhibit excellent ID calibration performance but often at the cost of classification accuracy. Moreover, these methods are not calibrated for the entire feature space, leading to overconfidence in the case of out-of-distribution (OOD) samples. In this paper, we leveraged the fact that deep models, including both random forests and deep-nets, learn internal representations which are unions of polytopes with affine activation functions to conceptualize them both as partitioning rules of the feature space. We replace the affine function in each polytope populated by the training data with a Gaussian kernel. We propose sufficient conditions for our proposed methods to be consistent estimators of the corresponding class conditional densities. Moreover, our experiments on both tabular and vision benchmarks show that the proposed approaches obtain well-calibrated posteriors while mostly preserving or improving the classification accuracy of the original algorithm for in-distribution region, and extrapolates beyond the training data to handle out-of-distribution inputs appropriately.

Graph Neural Networks (GNNs) have shown state-of-the-art improvements in node classification tasks on graphs. While these improvements have been largely demonstrated in a multi-class classification scenario, a more general and realistic scenario in which each node could have multiple labels has so far received little attention. The first challenge in conducting focused studies on multi-label node classification is the limited number of publicly available multi-label graph datasets. Therefore, as our first contribution, we collect and release three real-world biological datasets and develop a multi-label graph generator to generate datasets with tunable properties. While high label similarity (high homophily) is usually attributed to the success of GNNs, we argue that a multi-label scenario does not follow the usual semantics of homophily and heterophily so far defined for a multi-class scenario. As our second contribution, we define homophily and Cross-Class Neighborhood Similarity for the multi-label scenario and provide a thorough analyses of the collected $9$ multi-label datasets. Finally, we perform a large-scale comparative study with $8$ methods and $9$ datasets and analyse the performances of the methods to assess the progress made by current state of the art in the multi-label node classification scenario. We release our benchmark at https://github.com/Tianqi-py/MLGNC.

When learning graph neural networks (GNNs) in node-level prediction tasks, most existing loss functions are applied for each node independently, even if node embeddings and their labels are non-i.i.d. because of their graph structures. To eliminate such inconsistency, in this study we propose a novel Quasi-Wasserstein (QW) loss with the help of the optimal transport defined on graphs, leading to new learning and prediction paradigms of GNNs. In particular, we design a "Quasi-Wasserstein" distance between the observed multi-dimensional node labels and their estimations, optimizing the label transport defined on graph edges. The estimations are parameterized by a GNN in which the optimal label transport may determine the graph edge weights optionally. By reformulating the strict constraint of the label transport to a Bregman divergence-based regularizer, we obtain the proposed Quasi-Wasserstein loss associated with two efficient solvers learning the GNN together with optimal label transport. When predicting node labels, our model combines the output of the GNN with the residual component provided by the optimal label transport, leading to a new transductive prediction paradigm. Experiments show that the proposed QW loss applies to various GNNs and helps to improve their performance in node-level classification and regression tasks.

Distributed quantum computing is a promising computational paradigm for performing computations that are beyond the reach of individual quantum devices. Privacy in distributed quantum computing is critical for maintaining confidentiality and protecting the data in the presence of untrusted computing nodes. In this work, we introduce novel blind quantum machine learning protocols based on the quantum bipartite correlator algorithm. Our protocols have reduced communication overhead while preserving the privacy of data from untrusted parties. We introduce robust algorithm-specific privacy-preserving mechanisms with low computational overhead that do not require complex cryptographic techniques. We then validate the effectiveness of the proposed protocols through complexity and privacy analysis. Our findings pave the way for advancements in distributed quantum computing, opening up new possibilities for privacy-aware machine learning applications in the era of quantum technologies.

Large Language Models (LLMs) are increasingly deployed as the backend for a variety of real-world applications called LLM-Integrated Applications. Multiple recent works showed that LLM-Integrated Applications are vulnerable to prompt injection attacks, in which an attacker injects malicious instruction/data into the input of those applications such that they produce results as the attacker desires. However, existing works are limited to case studies. As a result, the literature lacks a systematic understanding of prompt injection attacks and their defenses. We aim to bridge the gap in this work. In particular, we propose a general framework to formalize prompt injection attacks. Existing attacks, which are discussed in research papers and blog posts, are special cases in our framework. Our framework enables us to design a new attack by combining existing attacks. Moreover, we also propose a framework to systematize defenses against prompt injection attacks. Using our frameworks, we conduct a systematic evaluation on prompt injection attacks and their defenses with 10 LLMs and 7 tasks. We hope our frameworks can inspire future research in this field. Our code is available at https://github.com/liu00222/Open-Prompt-Injection.

Adaptable models could greatly benefit robotic agents operating in the real world, allowing them to deal with novel and varying conditions. While approaches such as Bayesian inference are well-studied frameworks for adapting models to evidence, we build on recent advances in deep generative models which have greatly affected many areas of robotics. Harnessing modern GPU acceleration, we investigate how to quickly adapt the sample generation of neural network models to observations in robotic tasks. We propose a simple and general method that is applicable to various deep generative models and robotic environments. The key idea is to quickly fine-tune the model by fitting it to generated samples matching the observed evidence, using the cross-entropy method. We show that our method can be applied to both autoregressive models and variational autoencoders, and demonstrate its usability in object shape inference from grasping, inverse kinematics calculation, and point cloud completion.

On-device machine learning (ML) enables the training process to exploit a massive amount of user-generated private data samples. To enjoy this benefit, inter-device communication overhead should be minimized. With this end, we propose federated distillation (FD), a distributed model training algorithm whose communication payload size is much smaller than a benchmark scheme, federated learning (FL), particularly when the model size is large. Moreover, user-generated data samples are likely to become non-IID across devices, which commonly degrades the performance compared to the case with an IID dataset. To cope with this, we propose federated augmentation (FAug), where each device collectively trains a generative model, and thereby augments its local data towards yielding an IID dataset. Empirical studies demonstrate that FD with FAug yields around 26x less communication overhead while achieving 95-98% test accuracy compared to FL.

In order to trust the predictions of a machine learning algorithm, it is necessary to understand the factors that contribute to those predictions. In the case of probabilistic and uncertainty-aware models, it is necessary to understand not only the reasons for the predictions themselves, but also the model's level of confidence in those predictions. In this paper, we show how existing methods in explainability can be extended to uncertainty-aware models and how such extensions can be used to understand the sources of uncertainty in a model's predictive distribution. In particular, by adapting permutation feature importance, partial dependence plots, and individual conditional expectation plots, we demonstrate that novel insights into model behaviour may be obtained and that these methods can be used to measure the impact of features on both the entropy of the predictive distribution and the log-likelihood of the ground truth labels under that distribution. With experiments using both synthetic and real-world data, we demonstrate the utility of these approaches in understanding both the sources of uncertainty and their impact on model performance.

In this article we present new results on neural networks with linear threshold activation functions. We precisely characterize the class of functions that are representable by such neural networks and show that 2 hidden layers are necessary and sufficient to represent any function representable in the class. This is a surprising result in the light of recent exact representability investigations for neural networks using other popular activation functions like rectified linear units (ReLU). We also give precise bounds on the sizes of the neural networks required to represent any function in the class. Finally, we design an algorithm to solve the empirical risk minimization (ERM) problem to global optimality for these neural networks with a fixed architecture. The algorithm's running time is polynomial in the size of the data sample, if the input dimension and the size of the network architecture are considered fixed constants. The algorithm is unique in the sense that it works for any architecture with any number of layers, whereas previous polynomial time globally optimal algorithms work only for very restricted classes of architectures. Using these insights, we propose a new class of neural networks that we call shortcut linear threshold networks. To the best of our knowledge, this way of designing neural networks has not been explored before in the literature. We show that these neural networks have several desirable theoretical properties.

Due to the increasing use of Machine Learning models in high stakes decision making settings, it has become increasingly important to have tools to understand how models arrive at decisions. Assuming a trained Supervised Classification model, explanations can be obtained via counterfactual analysis: a counterfactual explanation of an instance indicates how this instance should be minimally modified so that the perturbed instance is classified in the desired class by the Machine Learning classification model. Most of the Counterfactual Analysis literature focuses on the single-instance single-counterfactual setting, in which the analysis is done for one single instance to provide one single explanation. Taking a stakeholder's perspective, in this paper we introduce the so-called collective counterfactual explanations. By means of novel Mathematical Optimization models, we provide a counterfactual explanation for each instance in a group of interest, so that the total cost of the perturbations is minimized under some linking constraints. Making the process of constructing counterfactuals collective instead of individual enables us to detect the features that are critical to the entire dataset to have the individuals classified in the desired class. Our methodology allows for some instances to be treated individually, performing the collective counterfactual analysis for a fraction of records of the group of interest. This way, outliers are identified and handled appropriately. Under some assumptions on the classifier and the space in which counterfactuals are sought, finding collective counterfactuals is reduced to solving a convex quadratic linearly constrained mixed integer optimization problem, which, for datasets of moderate size, can be solved to optimality using existing solvers. The performance of our approach is illustrated on real-world datasets, demonstrating its usefulness.

Existing hierarchical forecasting techniques scale poorly when the number of time series increases. We propose to learn a coherent forecast for millions of time series with a single bottom-level forecast model by using a sparse loss function that directly optimizes the hierarchical product and/or temporal structure. The benefit of our sparse hierarchical loss function is that it provides practitioners a method of producing bottom-level forecasts that are coherent to any chosen cross-sectional or temporal hierarchy. In addition, removing the need for a post-processing step as required in traditional hierarchical forecasting techniques reduces the computational cost of the prediction phase in the forecasting pipeline. On the public M5 dataset, our sparse hierarchical loss function performs up to 10% (RMSE) better compared to the baseline loss function. We implement our sparse hierarchical loss function within an existing forecasting model at bol, a large European e-commerce platform, resulting in an improved forecasting performance of 2% at the product level. Finally, we found an increase in forecasting performance of about 5-10% when evaluating the forecasting performance across the cross-sectional hierarchies that we defined. These results demonstrate the usefulness of our sparse hierarchical loss applied to a production forecasting system at a major e-commerce platform.

While the accuracy-fairness trade-off has been frequently observed in the literature of fair machine learning, rigorous theoretical analyses have been scarce. To demystify this long-standing challenge, this work seeks to develop a theoretical framework by characterizing the shape of the accuracy-fairness trade-off Pareto frontier (FairFrontier), determined by a set of all optimal Pareto classifiers that no other classifiers can dominate. Specifically, we first demonstrate the existence of the trade-off in real-world scenarios and then propose four potential categories to characterize the important properties of the accuracy-fairness Pareto frontier. For each category, we identify the necessary conditions that lead to corresponding trade-offs. Experimental results on synthetic data suggest insightful findings of the proposed framework: (1) When sensitive attributes can be fully interpreted by non-sensitive attributes, FairFrontier is mostly continuous. (2) Accuracy can suffer a \textit{sharp} decline when over-pursuing fairness. (3) Eliminate the trade-off via a two-step streamlined approach. The proposed research enables an in-depth understanding of the accuracy-fairness trade-off, pushing current fair machine-learning research to a new frontier.

During Automated Program Repair (APR), it can be challenging to synthesize correct patches for real-world systems in general-purpose programming languages. Recent Large Language Models (LLMs) have been shown to be helpful "copilots" in assisting developers with various coding tasks, and have also been directly applied for patch synthesis. However, most LLMs treat programs as sequences of tokens, meaning that they are ignorant of the underlying semantics constraints of the target programming language. This results in plenty of statically invalid generated patches, impeding the practicality of the technique. Therefore, we propose Repilot, a framework to further copilot the AI "copilots" (i.e., LLMs) by synthesizing more valid patches during the repair process. Our key insight is that many LLMs produce outputs autoregressively (i.e., token by token), resembling human writing programs, which can be significantly boosted and guided through a Completion Engine. Repilot synergistically synthesizes a candidate patch through the interaction between an LLM and a Completion Engine, which 1) prunes away infeasible tokens suggested by the LLM and 2) proactively completes the token based on the suggestions provided by the Completion Engine. Our evaluation on a subset of the widely-used Defects4j 1.2 and 2.0 datasets shows that Repilot fixes 66 and 50 bugs, respectively, surpassing the best-performing baseline by 14 and 16 bugs fixed. More importantly, Repilot is capable of producing more valid and correct patches than the base LLM when given the same generation budget.

Implementations of Markov chain Monte Carlo (MCMC) methods need to confront two fundamental challenges: accurate representation of prior information and efficient evaluation of likelihoods. Principal component analysis (PCA) and related techniques can in some cases facilitate the definition and sampling of the prior distribution, as well as the training of accurate surrogate models, using for instance, polynomial chaos expansion (PCE). However, complex geological priors with sharp contrasts necessitate more complex dimensionality-reduction techniques, such as, deep generative models (DGMs). By sampling a low-dimensional prior probability distribution defined in the low-dimensional latent space of such a model, it becomes possible to efficiently sample the physical domain at the price of a generator that is typically highly non-linear. Training a surrogate that is capable of capturing intricate non-linear relationships between latent parameters and outputs of forward modeling presents a notable challenge. Indeed, while PCE models provide high accuracy when the input-output relationship can be effectively approximated by relatively low-degree multivariate polynomials, this condition is typically not met when employing latent variables derived from DGMs. In this contribution, we present a strategy combining the excellent reconstruction performances of a variational autoencoder (VAE) with the accuracy of PCA-PCE surrogate modeling in the context of Bayesian ground penetrating radar (GPR) traveltime tomography. Within the MCMC process, the parametrization of the VAE is leveraged for prior exploration and sample proposals. Concurrently, surrogate modeling is conducted using PCE, which operates on either globally or locally defined principal components of the VAE samples under examination.

Studying language models (LMs) in terms of well-understood formalisms allows us to precisely characterize their abilities and limitations. Previous work has investigated the representational capacity of recurrent neural network (RNN) LMs in terms of their capacity to recognize unweighted formal languages. However, LMs do not describe unweighted formal languages -- rather, they define probability distributions over strings. In this work, we study what classes of such probability distributions RNN LMs can represent, which allows us to make more direct statements about their capabilities. We show that simple RNNs are equivalent to a subclass of probabilistic finite-state automata, and can thus model a strict subset of probability distributions expressible by finite-state models. Furthermore, we study the space complexity of representing finite-state LMs with RNNs. We show that, to represent an arbitrary deterministic finite-state LM with $N$ states over an alphabet $\Sigma$, an RNN requires $\Omega\left(N |\Sigma|\right)$ neurons. These results present a first step towards characterizing the classes of distributions RNN LMs can represent and thus help us understand their capabilities and limitations.

We introduce Cosmos, a framework for object-centric world modeling that is designed for compositional generalization (CG), i.e., high performance on unseen input scenes obtained through the composition of known visual "atoms." The central insight behind Cosmos is the use of a novel form of neurosymbolic grounding. Specifically, the framework introduces two new tools: (i) neurosymbolic scene encodings, which represent each entity in a scene using a real vector computed using a neural encoder, as well as a vector of composable symbols describing attributes of the entity, and (ii) a neurosymbolic attention mechanism that binds these entities to learned rules of interaction. Cosmos is end-to-end differentiable; also, unlike traditional neurosymbolic methods that require representations to be manually mapped to symbols, it computes an entity's symbolic attributes using vision-language foundation models. Through an evaluation that considers two different forms of CG on an established blocks-pushing domain, we show that the framework establishes a new state-of-the-art for CG in world modeling.

Modern detection transformers (DETRs) use a set of object queries to predict a list of bounding boxes, sort them by their classification confidence scores, and select the top-ranked predictions as the final detection results for the given input image. A highly performant object detector requires accurate ranking for the bounding box predictions. For DETR-based detectors, the top-ranked bounding boxes suffer from less accurate localization quality due to the misalignment between classification scores and localization accuracy, thus impeding the construction of high-quality detectors. In this work, we introduce a simple and highly performant DETR-based object detector by proposing a series of rank-oriented designs, combinedly called Rank-DETR. Our key contributions include: (i) a rank-oriented architecture design that can prompt positive predictions and suppress the negative ones to ensure lower false positive rates, as well as (ii) a rank-oriented loss function and matching cost design that prioritizes predictions of more accurate localization accuracy during ranking to boost the AP under high IoU thresholds. We apply our method to improve the recent SOTA methods (e.g., H-DETR and DINO-DETR) and report strong COCO object detection results when using different backbones such as ResNet-$50$, Swin-T, and Swin-L, demonstrating the effectiveness of our approach. Code is available at \url{https://github.com/LeapLabTHU/Rank-DETR}.

Insurers usually turn to generalized linear models for modelling claim frequency and severity data. Due to their success in other fields, machine learning techniques are gaining popularity within the actuarial toolbox. Our paper contributes to the literature on frequency-severity insurance pricing with machine learning via deep learning structures. We present a benchmark study on four insurance data sets with frequency and severity targets in the presence of multiple types of input features. We compare in detail the performance of: a generalized linear model on binned input data, a gradient-boosted tree model, a feed-forward neural network (FFNN), and the combined actuarial neural network (CANN). Our CANNs combine a baseline prediction established with a GLM and GBM, respectively, with a neural network correction. We explain the data preprocessing steps with specific focus on the multiple types of input features typically present in tabular insurance data sets, such as postal codes, numeric and categorical covariates. Autoencoders are used to embed the categorical variables into the neural network and we explore their potential advantages in a frequency-severity setting. Finally, we construct global surrogate models for the neural nets' frequency and severity models. These surrogates enable the translation of the essential insights captured by the FFNNs or CANNs to GLMs. As such, a technical tariff table results that can easily be deployed in practice.

Modeling customer shopping intentions is a crucial task for e-commerce, as it directly impacts user experience and engagement. Thus, accurately understanding customer preferences is essential for providing personalized recommendations. Session-based recommendation, which utilizes customer session data to predict their next interaction, has become increasingly popular. However, existing session datasets have limitations in terms of item attributes, user diversity, and dataset scale. As a result, they cannot comprehensively capture the spectrum of user behaviors and preferences. To bridge this gap, we present the Amazon Multilingual Multi-locale Shopping Session Dataset, namely Amazon-M2. It is the first multilingual dataset consisting of millions of user sessions from six different locales, where the major languages of products are English, German, Japanese, French, Italian, and Spanish. Remarkably, the dataset can help us enhance personalization and understanding of user preferences, which can benefit various existing tasks as well as enable new tasks. To test the potential of the dataset, we introduce three tasks in this work: (1) next-product recommendation, (2) next-product recommendation with domain shifts, and (3) next-product title generation. With the above tasks, we benchmark a range of algorithms on our proposed dataset, drawing new insights for further research and practice. In addition, based on the proposed dataset and tasks, we hosted a competition in the KDD CUP 2023 and have attracted thousands of users and submissions. The winning solutions and the associated workshop can be accessed at our website https://kddcup23.github.io/.

Federated Learning (FL) is a machine learning paradigm, which enables multiple and decentralized clients to collaboratively train a model under the orchestration of a central aggregator. Traditional FL solutions rely on the trust assumption of the centralized aggregator, which forms cohorts of clients in a fair and honest manner. However, a malicious aggregator, in reality, could abandon and replace the client's training models, or launch Sybil attacks to insert fake clients. Such malicious behaviors give the aggregator more power to control clients in the FL setting and determine the final training results. In this work, we introduce zkFL, which leverages zero-knowledge proofs (ZKPs) to tackle the issue of a malicious aggregator during the training model aggregation process. To guarantee the correct aggregation results, the aggregator needs to provide a proof per round. The proof can demonstrate to the clients that the aggregator executes the intended behavior faithfully. To further reduce the verification cost of clients, we employ a blockchain to handle the proof in a zero-knowledge way, where miners (i.e., the nodes validating and maintaining the blockchain data) can verify the proof without knowing the clients' local and aggregated models. The theoretical analysis and empirical results show that zkFL can achieve better security and privacy than traditional FL, without modifying the underlying FL network structure or heavily compromising the training speed.

Unraveling the emergence of collective learning in systems of coupled artificial neural networks is an endeavor with broader implications for physics, machine learning, neuroscience and society. Here we introduce a minimal model that condenses several recent decentralized algorithms by considering a competition between two terms: the local learning dynamics in the parameters of each neural network unit, and a diffusive coupling among units that tends to homogenize the parameters of the ensemble. We derive the coarse-grained behavior of our model via an effective theory for linear networks that we show is analogous to a deformed Ginzburg-Landau model with quenched disorder. This framework predicts (depth-dependent) disorder-order-disorder phase transitions in the parameters' solutions that reveal the onset of a collective learning phase, along with a depth-induced delay of the critical point and a robust shape of the microscopic learning path. We validate our theory in realistic ensembles of coupled nonlinear networks trained in the MNIST dataset under privacy constraints. Interestingly, experiments confirm that individual networks -- trained only with private data -- can fully generalize to unseen data classes when the collective learning phase emerges. Our work elucidates the physics of collective learning and contributes to the mechanistic interpretability of deep learning in decentralized settings.

This paper analyses a set of simple adaptations that transform standard message-passing Graph Neural Networks (GNN) into provably powerful directed multigraph neural networks. The adaptations include multigraph port numbering, ego IDs, and reverse message passing. We prove that the combination of these theoretically enables the detection of any directed subgraph pattern. To validate the effectiveness of our proposed adaptations in practice, we conduct experiments on synthetic subgraph detection tasks, which demonstrate outstanding performance with almost perfect results. Moreover, we apply our proposed adaptations to two financial crime analysis tasks. We observe dramatic improvements in detecting money laundering transactions, improving the minority-class F1 score of a standard message-passing GNN by up to 30%, and closely matching or outperforming tree-based and GNN baselines. Similarly impressive results are observed on a real-world phishing detection dataset, boosting three standard GNNs' F1 scores by around 15% and outperforming all baselines.

Stochastic processes defined on integer valued state spaces are popular within the physical and biological sciences. These models are necessary for capturing the dynamics of small systems where the individual nature of the populations cannot be ignored and stochastic effects are important. The inference of the parameters of such models, from time series data, is difficult due to intractability of the likelihood; current methods, based on simulations of the underlying model, can be so computationally expensive as to be prohibitive. In this paper we construct a neural likelihood approximation for integer valued time series data using causal convolutions, which allows us to evaluate the likelihood of the whole time series in parallel. We demonstrate our method by performing inference on a number of ecological and epidemiological models, showing that we can accurately approximate the true posterior while achieving significant computational speed ups in situations where current methods struggle.

We propose inverse renormalization group transformations to construct approximate configurations for lattice volumes that have not yet been accessed by supercomputers or large-scale simulations in the study of spin glasses. Specifically, starting from lattices of volume $V=8^{3}$ in the case of the three-dimensional Edwards-Anderson model we employ machine learning algorithms to construct rescaled lattices up to $V'=128^{3}$, which we utilize to extract two critical exponents. We conclude by discussing how to incorporate numerical exactness within inverse renormalization group approaches of disordered systems, thus opening up the opportunity to explore a sustainable and energy-efficient generation of exact configurations for increasing lattice volumes without the use of dedicated supercomputers.

Compressing neural networks is a key step when deploying models for real-time or embedded applications. Factorizing the model's matrices using low-rank approximations is a promising method for achieving compression. While it is possible to set the rank before training, this approach is neither flexible nor optimal. In this work, we propose a post-training rank-selection method called Rank-Tuning that selects a different rank for each matrix. Used in combination with training adaptations, our method achieves high compression rates with no or little performance degradation. Our numerical experiments on signal processing tasks show that we can compress recurrent neural networks up to 14x with at most 1.4% relative performance reduction.

Traffic state forecasting is crucial for traffic management and control strategies, as well as user- and system-level decision making in the transportation network. While traffic forecasting has been approached with a variety of techniques over the last couple of decades, most approaches simply rely on endogenous traffic variables for state prediction, despite the evidence that exogenous factors can significantly impact traffic conditions. This paper proposes a multi-dimensional spatio-temporal graph attention-based traffic prediction approach (M-STGAT), which predicts traffic based on past observations of speed, along with lane closure events, temperature, and visibility across the transportation network. The approach is based on a graph attention network architecture, which also learns based on the structure of the transportation network on which these variables are observed. Numerical experiments are performed using traffic speed and lane closure data from the California Department of Transportation (Caltrans) Performance Measurement System (PeMS). The corresponding weather data were downloaded from the National Oceanic and Atmospheric Administration (NOOA) Automated Surface Observing Systems (ASOS). For comparison, the numerical experiments implement three alternative models which do not allow for the multi-dimensional input. The M-STGAT is shown to outperform the three alternative models, when performing tests using our primary data set for prediction with a 30-, 45-, and 60-minute prediction horizon, in terms of three error measures: Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). However, the model's transferability can vary for different transfer data sets and this aspect may require further investigation.

Time is one of the crucial factors in real-world question answering (QA) problems. However, language models have difficulty understanding the relationships between time specifiers, such as 'after' and 'before', and numbers, since existing QA datasets do not include sufficient time expressions. To address this issue, we propose a Time-Context aware Question Answering (TCQA) framework. We suggest a Time-Context dependent Span Extraction (TCSE) task, and build a time-context dependent data generation framework for model training. Moreover, we present a metric to evaluate the time awareness of the QA model using TCSE. The TCSE task consists of a question and four sentence candidates classified as correct or incorrect based on time and context. The model is trained to extract the answer span from the sentence that is both correct in time and context. The model trained with TCQA outperforms baseline models up to 8.5 of the F1-score in the TimeQA dataset. Our dataset and code are available at https://github.com/sonjbin/TCQA

We introduce Voyager, the first LLM-powered embodied lifelong learning agent in Minecraft that continuously explores the world, acquires diverse skills, and makes novel discoveries without human intervention. Voyager consists of three key components: 1) an automatic curriculum that maximizes exploration, 2) an ever-growing skill library of executable code for storing and retrieving complex behaviors, and 3) a new iterative prompting mechanism that incorporates environment feedback, execution errors, and self-verification for program improvement. Voyager interacts with GPT-4 via blackbox queries, which bypasses the need for model parameter fine-tuning. The skills developed by Voyager are temporally extended, interpretable, and compositional, which compounds the agent's abilities rapidly and alleviates catastrophic forgetting. Empirically, Voyager shows strong in-context lifelong learning capability and exhibits exceptional proficiency in playing Minecraft. It obtains 3.3x more unique items, travels 2.3x longer distances, and unlocks key tech tree milestones up to 15.3x faster than prior SOTA. Voyager is able to utilize the learned skill library in a new Minecraft world to solve novel tasks from scratch, while other techniques struggle to generalize. We open-source our full codebase and prompts at https://voyager.minedojo.org/.

The Orthogonal-Time-Frequency-Space (OTFS) signaling is known to be resilient to doubly-dispersive channels, which impacts high mobility scenarios. On the other hand, the Orthogonal-Frequency-Division-Multiplexing (OFDM) waveforms enjoy the benefits of the reuse of legacy architectures, simplicity of receiver design, and low-complexity detection. Several studies that compare the performance of OFDM and OTFS have indicated mixed outcomes due to the plethora of system parameters at play beyond high-mobility conditions. In this work, we exemplify this observation using simulations and propose a deep neural network (DNN)-based adaptation scheme to switch between using either an OTFS or OFDM signal processing chain at the transmitter and receiver for optimal mean-squared-error (MSE) performance. The DNN classifier is trained to switch between the two schemes by observing the channel condition, received SNR, and modulation format. We compare the performance of the OTFS, OFDM, and the proposed switched-waveform scheme. The simulations indicate superior performance with the proposed scheme with a well-trained DNN, thus improving the MSE performance of the communication significantly.

The cause-to-effect analysis can help us decompose all the likely causes of a problem, such as an undesirable business situation or unintended harm to the individual(s). This implies that we can identify how the problems are inherited, rank the causes to help prioritize fixes, simplify a complex problem and visualize them. In the context of machine learning (ML), one can use cause-to-effect analysis to understand the reason for the biased behavior of the system. For example, we can examine the root causes of biases by checking each feature for a potential cause of bias in the model. To approach this, one can apply small changes to a given feature or a pair of features in the data, following some guidelines and observing how it impacts the decision made by the model (i.e., model prediction). Therefore, we can use cause-to-effect analysis to identify the potential bias-inducing features, even when these features are originally are unknown. This is important since most current methods require a pre-identification of sensitive features for bias assessment and can actually miss other relevant bias-inducing features, which is why systematic identification of such features is necessary. Moreover, it often occurs that to achieve an equitable outcome, one has to take into account sensitive features in the model decision. Therefore, it should be up to the domain experts to decide based on their knowledge of the context of a decision whether bias induced by specific features is acceptable or not. In this study, we propose an approach for systematically identifying all bias-inducing features of a model to help support the decision-making of domain experts. We evaluated our technique using four well-known datasets to showcase how our contribution can help spearhead the standard procedure when developing, testing, maintaining, and deploying fair/equitable machine learning systems.

Classes of target functions containing a large number of approximately orthogonal elements are known to be hard to learn by the Statistical Query algorithms. Recently this classical fact re-emerged in a theory of gradient-based optimization of neural networks. In the novel framework, the hardness of a class is usually quantified by the variance of the gradient with respect to a random choice of a target function. A set of functions of the form $x\to ax \bmod p$, where $a$ is taken from ${\mathbb Z}_p$, has attracted some attention from deep learning theorists and cryptographers recently. This class can be understood as a subset of $p$-periodic functions on ${\mathbb Z}$ and is tightly connected with a class of high-frequency periodic functions on the real line. We present a mathematical analysis of limitations and challenges associated with using gradient-based learning techniques to train a high-frequency periodic function or modular multiplication from examples. We highlight that the variance of the gradient is negligibly small in both cases when either a frequency or the prime base $p$ is large. This in turn prevents such a learning algorithm from being successful.

The prevalence and strong capability of large language models (LLMs) present significant safety and ethical risks if exploited by malicious users. To prevent the potentially deceptive usage of LLMs, recent works have proposed algorithms to detect LLM-generated text and protect LLMs. In this paper, we investigate the robustness and reliability of these LLM detectors under adversarial attacks. We study two types of attack strategies: 1) replacing certain words in an LLM's output with their synonyms given the context; 2) automatically searching for an instructional prompt to alter the writing style of the generation. In both strategies, we leverage an auxiliary LLM to generate the word replacements or the instructional prompt. Different from previous works, we consider a challenging setting where the auxiliary LLM can also be protected by a detector. Experiments reveal that our attacks effectively compromise the performance of all detectors in the study with plausible generations, underscoring the urgent need to improve the robustness of LLM-generated text detection systems.

Advances in the field of vision-language contrastive learning have made it possible for many downstream applications to be carried out efficiently and accurately by simply taking the dot product between image and text representations. One of the most representative approaches proposed recently known as CLIP has garnered widespread adoption due to its effectiveness. CLIP is trained with an InfoNCE loss that takes into account both positive and negative samples to help learn a much more robust representation space. This paper reveals that the common downstream practice of taking a dot product is only a zeroth-order approximation of the optimization goal, resulting in a loss of information during test-time. Intuitively, since the model has been optimized based on the InfoNCE loss, test-time procedures should also be in alignment. The question lies in how one can retrieve any semblance of negative samples information during inference in a computationally efficient way. To this end, we propose Distribution Normalization (DN), where we approximate the mean representation of a batch of test samples and use such a mean to represent what would be analogous to negative samples in the InfoNCE loss. DN requires no retraining or fine-tuning and can be effortlessly applied during inference. Extensive experiments on a wide variety of downstream tasks exhibit a clear advantage of DN over the dot product on top of other existing test-time augmentation methods.

Existing private synthetic data generation algorithms are agnostic to downstream tasks. However, end users may have specific requirements that the synthetic data must satisfy. Failure to meet these requirements could significantly reduce the utility of the data for downstream use. We introduce a post-processing technique that improves the utility of the synthetic data with respect to measures selected by the end user, while preserving strong privacy guarantees and dataset quality. Our technique involves resampling from the synthetic data to filter out samples that do not meet the selected utility measures, using an efficient stochastic first-order algorithm to find optimal resampling weights. Through comprehensive numerical experiments, we demonstrate that our approach consistently improves the utility of synthetic data across multiple benchmark datasets and state-of-the-art synthetic data generation algorithms.

Physics-informed neural networks have emerged as a coherent framework for building predictive models that combine statistical patterns with domain knowledge. The underlying notion is to enrich the optimization loss function with known relationships to constrain the space of possible solutions. Hydrodynamic simulations are a core constituent of modern cosmology, while the required computations are both expensive and time-consuming. At the same time, the comparatively fast simulation of dark matter requires fewer resources, which has led to the emergence of machine learning algorithms for baryon inpainting as an active area of research; here, recreating the scatter found in hydrodynamic simulations is an ongoing challenge. This paper presents the first application of physics-informed neural networks to baryon inpainting by combining advances in neural network architectures with physical constraints, injecting theory on baryon conversion efficiency into the model loss function. We also introduce a punitive prediction comparison based on the Kullback-Leibler divergence, which enforces scatter reproduction. By simultaneously extracting the complete set of baryonic properties for the Simba suite of cosmological simulations, our results demonstrate improved accuracy of baryonic predictions based on dark matter halo properties, successful recovery of the fundamental metallicity relation, and retrieve scatter that traces the target simulation's distribution.

The digitization of manufacturing processes enables promising applications for machine learning-assisted quality assurance. A widely used manufacturing process that can strongly benefit from data-driven solutions is \ac{GMAW}. The welding process is characterized by complex cause-effect relationships between material properties, process conditions and weld quality. In non-laboratory environments with frequently changing process parameters, accurate determination of weld quality by destructive testing is economically unfeasible. Deep learning offers the potential to identify the relationships in available process data and predict the weld quality from process observations. In this paper, we present a concept for a deep learning based predictive quality system in \ac{GMAW}. At its core, the concept involves a pipeline consisting of four major phases: collection and management of multi-sensor data (e.g. current and voltage), real-time processing and feature engineering of the time series data by means of autoencoders, training and deployment of suitable recurrent deep learning models for quality predictions, and model evolutions under changing process conditions using continual learning. The concept provides the foundation for future research activities in which we will realize an online predictive quality system for running production.

Artificial intelligence (AI) systems possess significant potential to drive societal progress. However, their deployment often faces obstacles due to substantial safety concerns. Safe reinforcement learning (SafeRL) emerges as a solution to optimize policies while simultaneously adhering to multiple constraints, thereby addressing the challenge of integrating reinforcement learning in safety-critical scenarios. In this paper, we present an environment suite called Safety-Gymnasium, which encompasses safety-critical tasks in both single and multi-agent scenarios, accepting vector and vision-only input. Additionally, we offer a library of algorithms named Safe Policy Optimization (SafePO), comprising 16 state-of-the-art SafeRL algorithms. This comprehensive library can serve as a validation tool for the research community. By introducing this benchmark, we aim to facilitate the evaluation and comparison of safety performance, thus fostering the development of reinforcement learning for safer, more reliable, and responsible real-world applications. The website of this project can be accessed at https://sites.google.com/view/safety-gymnasium.

Large decoder-only language models (LMs) can be largely improved in terms of perplexity by retrieval (e.g., RETRO), but its impact on text generation quality and downstream task accuracy is unclear. Thus, it is still an open question: shall we pretrain large autoregressive LMs with retrieval? To answer it, we perform a comprehensive study on a scalable pre-trained retrieval-augmented LM (i.e., RETRO) compared with standard GPT and retrieval-augmented GPT incorporated at fine-tuning or inference stages. We first provide the recipe to reproduce RETRO up to 9.5B parameters while retrieving a text corpus with 330B tokens. Based on that, we have the following novel findings: i) RETRO outperforms GPT on text generation with much less degeneration (i.e., repetition), moderately higher factual accuracy, and slightly lower toxicity with a nontoxic retrieval database. ii) On the LM Evaluation Harness benchmark, RETRO largely outperforms GPT on knowledge-intensive tasks, but is on par with GPT on other tasks. Furthermore, we introduce a simple variant of the model, RETRO++, which largely improves open-domain QA results of original RETRO (e.g., EM score +8.6 on Natural Question) and significantly outperforms retrieval-augmented GPT in both fine-tuning and zero-shot evaluation settings. Our findings highlight the promising direction of pretraining autoregressive LMs with retrieval as future foundation models. We release our implementation at: https://github.com/NVIDIA/Megatron-LM#retro.

Deep Learning models, such as those used in an autonomous vehicle are vulnerable to adversarial attacks where an attacker could place an adversarial object in the environment, leading to mis-classification. Generating these adversarial objects in the digital space has been extensively studied, however successfully transferring these attacks from the digital realm to the physical realm has proven challenging when controlling for real-world environmental factors. In response to these limitations, we introduce REVAMP, an easy-to-use Python library that is the first-of-its-kind tool for creating attack scenarios with arbitrary objects and simulating realistic environmental factors, lighting, reflection, and refraction. REVAMP enables researchers and practitioners to swiftly explore various scenarios within the digital realm by offering a wide range of configurable options for designing experiments and using differentiable rendering to reproduce physically plausible adversarial objects. We will demonstrate and invite the audience to try REVAMP to produce an adversarial texture on a chosen object while having control over various scene parameters. The audience will choose a scene, an object to attack, the desired attack class, and the number of camera positions to use. Then, in real time, we show how this altered texture causes the chosen object to be mis-classified, showcasing the potential of REVAMP in real-world scenarios. REVAMP is open-source and available at https://github.com/poloclub/revamp.

Diffusion models are powerful, but they require a lot of time and data to train. We propose Patch Diffusion, a generic patch-wise training framework, to significantly reduce the training time costs while improving data efficiency, which thus helps democratize diffusion model training to broader users. At the core of our innovations is a new conditional score function at the patch level, where the patch location in the original image is included as additional coordinate channels, while the patch size is randomized and diversified throughout training to encode the cross-region dependency at multiple scales. Sampling with our method is as easy as in the original diffusion model. Through Patch Diffusion, we could achieve $\mathbf{\ge 2\times}$ faster training, while maintaining comparable or better generation quality. Patch Diffusion meanwhile improves the performance of diffusion models trained on relatively small datasets, $e.g.$, as few as 5,000 images to train from scratch. We achieve outstanding FID scores in line with state-of-the-art benchmarks: 1.77 on CelebA-64$\times$64, 1.93 on AFHQv2-Wild-64$\times$64, and 2.72 on ImageNet-256$\times$256. We share our code and pre-trained models at https://github.com/Zhendong-Wang/Patch-Diffusion.

The training and generalization dynamics of the Transformer's core mechanism, namely the Attention mechanism, remain under-explored. Besides, existing analyses primarily focus on single-head attention. Inspired by the demonstrated benefits of overparameterization when training fully-connected networks, we investigate the potential optimization and generalization advantages of using multiple attention heads. Towards this goal, we derive convergence and generalization guarantees for gradient-descent training of a single-layer multi-head self-attention model, under a suitable realizability condition on the data. We then establish primitive conditions on the initialization that ensure realizability holds. Finally, we demonstrate that these conditions are satisfied for a simple tokenized-mixture model. We expect the analysis can be extended to various data-model and architecture variations.

Linear temporal logic (LTL) and omega-regular objectives -- a superset of LTL -- have seen recent use as a way to express non-Markovian objectives in reinforcement learning. We introduce a model-based probably approximately correct (PAC) learning algorithm for omega-regular objectives in Markov decision processes. Unlike prior approaches, our algorithm learns from sampled trajectories of the system and does not require prior knowledge of the system's topology.

We explain the methodology used to create the data submitted to HuMob Challenge, a data analysis competition for human mobility prediction. We adopted a personalized model to predict the individual's movement trajectory from their data, instead of predicting from the overall movement, based on the hypothesis that human movement is unique to each person. We devised the features such as the date and time, activity time, days of the week, time of day, and frequency of visits to POI (Point of Interest). As additional features, we incorporated the movement of other individuals with similar behavior patterns through the employment of clustering. The machine learning model we adopted was the Support Vector Regression (SVR). We performed accuracy through offline assessment and carried out feature selection and parameter tuning. Although overall dataset provided consists of 100,000 users trajectory, our method use only 20,000 target users data, and do not need to use other 80,000 data. Despite the personalized model's traditional feature engineering approach, this model yields reasonably good accuracy with lower computational cost.

Diffusion models have risen as a powerful tool in robotics due to their flexibility and multi-modality. While some of these methods effectively address complex problems, they often depend heavily on inference-time obstacle detection and require additional equipment. Addressing these challenges, we present a method that, during inference time, simultaneously generates only reachable goals and plans motions that avoid obstacles, all from a single visual input. Central to our approach is the novel use of a collision-avoiding diffusion kernel for training. Through evaluations against behavior-cloning and classical diffusion models, our framework has proven its robustness. It is particularly effective in multi-modal environments, navigating toward goals and avoiding unreachable ones blocked by obstacles, while ensuring collision avoidance.

We propose DyGFormer, a new Transformer-based architecture for dynamic graph learning. DyGFormer is conceptually simple and only needs to learn from nodes' historical first-hop interactions by: (1) a neighbor co-occurrence encoding scheme that explores the correlations of the source node and destination node based on their historical sequences; (2) a patching technique that divides each sequence into multiple patches and feeds them to Transformer, allowing the model to effectively and efficiently benefit from longer histories. We also introduce DyGLib, a unified library with standard training pipelines, extensible coding interfaces, and comprehensive evaluating protocols to promote reproducible, scalable, and credible dynamic graph learning research. By performing exhaustive experiments on thirteen datasets for dynamic link prediction and dynamic node classification tasks, we find that DyGFormer achieves state-of-the-art performance on most of the datasets, demonstrating its effectiveness in capturing nodes' correlations and long-term temporal dependencies. Moreover, some results of baselines are inconsistent with previous reports, which may be caused by their diverse but less rigorous implementations, showing the importance of DyGLib. All the used resources are publicly available at https://github.com/yule-BUAA/DyGLib.

Despite the recent growth of theoretical studies and empirical successes of neural networks, gradient backpropagation is still the most widely used algorithm for training such networks. On the one hand, we have deterministic or full gradient (FG) approaches that have a cost proportional to the amount of training data used but have a linear convergence rate, and on the other hand, stochastic gradient (SG) methods that have a cost independent of the size of the dataset, but have a less optimal convergence rate than the determinist approaches. To combine the cost of the stochastic approach with the convergence rate of the deterministic approach, a stochastic average gradient (SAG) has been proposed. SAG is a method for optimizing the sum of a finite number of smooth convex functions. Like SG methods, the SAG method's iteration cost is independent of the number of terms in the sum. In this work, we propose to compare SAG to some standard optimizers used in machine learning. SAG converges faster than other optimizers on simple toy problems and performs better than many other optimizers on simple machine learning problems. We also propose a combination of SAG with the momentum algorithm and Adam. These combinations allow empirically higher speed and obtain better performance than the other methods, especially when the landscape of the function to optimize presents obstacles or is ill-conditioned.

Entropy maximization and free energy minimization are general physical principles for modeling the dynamics of various physical systems. Notable examples include modeling decision-making within the brain using the free-energy principle, optimizing the accuracy-complexity trade-off when accessing hidden variables with the information bottleneck principle (Tishby et al., 2000), and navigation in random environments using information maximization (Vergassola et al., 2007). Built on this principle, we propose a new class of bandit algorithms that maximize an approximation to the information of a key variable within the system. To this end, we develop an approximated analytical physics-based representation of an entropy to forecast the information gain of each action and greedily choose the one with the largest information gain. This method yields strong performances in classical bandit settings. Motivated by its empirical success, we prove its asymptotic optimality for the two-armed bandit problem with Gaussian rewards. Owing to its ability to encompass the system's properties in a global physical functional, this approach can be efficiently adapted to more complex bandit settings, calling for further investigation of information maximization approaches for multi-armed bandit problems.

Binary classification is a fundamental task in machine learning, with applications spanning various scientific domains. Whether scientists are conducting fundamental research or refining practical applications, they typically assess and rank classification techniques based on performance metrics such as accuracy, sensitivity, and specificity. However, reported performance scores may not always serve as a reliable basis for research ranking. This can be attributed to undisclosed or unconventional practices related to cross-validation, typographical errors, and other factors. In a given experimental setup, with a specific number of positive and negative test items, most performance scores can assume specific, interrelated values. In this paper, we introduce numerical techniques to assess the consistency of reported performance scores and the assumed experimental setup. Importantly, the proposed approach does not rely on statistical inference but uses numerical methods to identify inconsistencies with certainty. Through three different applications related to medicine, we demonstrate how the proposed techniques can effectively detect inconsistencies, thereby safeguarding the integrity of research fields. To benefit the scientific community, we have made the consistency tests available in an open-source Python package.

Sequential transfer optimization (STO), which aims to improve the optimization performance on a task of interest by exploiting the knowledge captured from several previously-solved optimization tasks stored in a database, has been gaining increasing research attention over the years. However, despite the remarkable advances in algorithm design, the development of a systematic benchmark suite for comprehensive comparisons of STO algorithms received far less attention. Existing test problems are either simply generated by assembling other benchmark functions or extended from specific practical problems with limited scalability. The relationships between the optimal solutions of the source and target tasks in these problems are also often manually configured, limiting their ability to model different similarity relationships presented in real-world problems. Consequently, the good performance achieved by an algorithm on these problems might be biased and hard to be generalized to other problems. In light of the above, in this study, we first introduce four concepts for characterizing STO problems and present an important problem feature, namely similarity distribution, which quantitatively delineates the relationship between the optima of the source and target tasks. Then, we present the general design guidelines of STO problems and a particular STO problem generator with good scalability. Specifically, the similarity distribution of a problem can be easily customized, enabling a continuous spectrum of representation of the diverse similarity relationships of real-world problems. Lastly, a benchmark suite with 12 STO problems featured by a variety of customized similarity relationships is developed using the proposed generator. The source code of the problem generator is available at https://github.com/XmingHsueh/STOP-G.

This letter proposes a novel anti-interference technique, semantic interference cancellation (SemantIC), for enhancing information quality towards the sixth-generation (6G) wireless networks. SemantIC only requires the receiver to concatenate the channel decoder with a semantic auto-encoder. This constructs a turbo loop which iteratively and alternately eliminates noise in the signal domain and the semantic domain. From the viewpoint of network information theory, the neural network of the semantic auto-encoder stores side information by training, and provides side information in iterative decoding, as an implementation of the Wyner-Ziv theorem. Simulation results verify the performance improvement by SemantIC without extra channel resource cost.

Although remarkable progress has been achieved in preventing large language model (LLM) hallucinations using instruction tuning and retrieval augmentation, it remains challenging to measure the reliability of LLMs using human-crafted evaluation data which is not available for many tasks and domains and could suffer from data leakage. Inspired by adversarial machine learning, this paper aims to develop a method of automatically generating evaluation data by appropriately modifying existing data on which LLMs behave faithfully. Specifically, this paper presents AutoDebug, an LLM-based framework to use prompting chaining to generate transferable adversarial attacks in the form of question-answering examples. We seek to understand the extent to which these examples trigger the hallucination behaviors of LLMs. We implement AutoDebug using ChatGPT and evaluate the resulting two variants of a popular open-domain question-answering dataset, Natural Questions (NQ), on a collection of open-source and proprietary LLMs under various prompting settings. Our generated evaluation data is human-readable and, as we show, humans can answer these modified questions well. Nevertheless, we observe pronounced accuracy drops across multiple LLMs including GPT-4. Our experimental results show that LLMs are likely to hallucinate in two categories of question-answering scenarios where (1) there are conflicts between knowledge given in the prompt and their parametric knowledge, or (2) the knowledge expressed in the prompt is complex. Finally, we find that the adversarial examples generated by our method are transferable across all considered LLMs. The examples generated by a small model can be used to debug a much larger model, making our approach cost-effective.

We study the problem of lifelong graph learning in an open-world scenario, where a model needs to deal with new tasks and potentially unknown classes. We utilize Out-of-Distribution (OOD) detection methods to recognize new classes and adapt existing non-graph OOD detection methods to graph data. Crucially, we suggest performing new class detection by combining OOD detection methods with information aggregated from the graph neighborhood. Most OOD detection methods avoid determining a crisp threshold for deciding whether a vertex is OOD. To tackle this problem, we propose a Weakly-supervised Relevance Feedback (Open-WRF) method, which decreases the sensitivity to thresholds in OOD detection. We evaluate our approach on six benchmark datasets. Our results show that the proposed neighborhood aggregation method for OOD scores outperforms existing methods independent of the underlying graph neural network. Furthermore, we demonstrate that our Open-WRF method is more robust to threshold selection and analyze the influence of graph neighborhood on OOD detection. The aggregation and threshold methods are compatible with arbitrary graph neural networks and OOD detection methods, making our approach versatile and applicable to many real-world applications.

Argumentation is a very active research field of Artificial Intelligence concerned with the representation and evaluation of arguments used in dialogues between humans and/or artificial agents. Acceptability semantics of formal argumentation systems define the criteria for the acceptance or rejection of arguments. Several software systems, known as argumentation solvers, have been developed to compute the accepted/rejected arguments using such criteria. These include systems that learn to identify the accepted arguments using non-interpretable methods. In this paper we present a novel framework, which uses an Inductive Logic Programming approach to learn the acceptability semantics for several abstract and structured argumentation frameworks in an interpretable way. Through an empirical evaluation we show that our framework outperforms existing argumentation solvers, thus opening up new future research directions in the area of formal argumentation and human-machine dialogues.

This paper examines the use of operator-theoretic approaches to the analysis of chaotic systems through the lens of their unstable periodic orbits (UPOs). Our approach involves three data-driven steps for detecting, identifying, and stabilizing UPOs. We demonstrate the use of kernel integral operators within delay coordinates as an innovative method for UPO detection. For identifying the dynamic behavior associated with each individual UPO, we utilize the Koopman operator to present the dynamics as linear equations in the space of Koopman eigenfunctions. This allows for characterizing the chaotic attractor by investigating its principal dynamical modes across varying UPOs. We extend this methodology into an interpretable machine learning framework aimed at stabilizing strange attractors on their UPOs. To illustrate the efficacy of our approach, we apply it to the Lorenz attractor as a case study.

We present a novel meta-learning approach for 6D pose estimation on unknown objects. In contrast to ``instance-level" and ``category-level" pose estimation methods, our algorithm learns object representation in a category-agnostic way, which endows it with strong generalization capabilities across object categories. Specifically, we employ a neural process-based meta-learning approach to train an encoder to capture texture and geometry of an object in a latent representation, based on very few RGB-D images and ground-truth keypoints. The latent representation is then used by a simultaneously meta-trained decoder to predict the 6D pose of the object in new images. Furthermore, we propose a novel geometry-aware decoder for the keypoint prediction using a Graph Neural Network (GNN), which explicitly takes geometric constraints specific to each object into consideration. To evaluate our algorithm, extensive experiments are conducted on the \linemod dataset, and on our new fully-annotated synthetic datasets generated from Multiple Categories in Multiple Scenes (MCMS). Experimental results demonstrate that our model performs well on unseen objects with very different shapes and appearances. Remarkably, our model also shows robust performance on occluded scenes although trained fully on data without occlusion. To our knowledge, this is the first work exploring \textbf{cross-category level} 6D pose estimation.

The fast-paced development of machine learning (ML) methods coupled with its increasing adoption in research poses challenges for researchers without extensive training in ML. In neuroscience, for example, ML can help understand brain-behavior relationships, diagnose diseases, and develop biomarkers using various data sources like magnetic resonance imaging and electroencephalography. The primary objective of ML is to build models that can make accurate predictions on unseen data. Researchers aim to prove the existence of such generalizable models by evaluating performance using techniques such as cross-validation (CV), which uses systematic subsampling to estimate the generalization performance. Choosing a CV scheme and evaluating an ML pipeline can be challenging and, if used improperly, can lead to overestimated results and incorrect interpretations. We created julearn, an open-source Python library, that allow researchers to design and evaluate complex ML pipelines without encountering in common pitfalls. In this manuscript, we present the rationale behind julearn's design, its core features, and showcase three examples of previously-published research projects that can be easily implemented using this novel library. Julearn aims to simplify the entry into the ML world by providing an easy-to-use environment with built in guards against some of the most common ML pitfalls. With its design, unique features and simple interface, it poses as a useful Python-based library for research projects.

We propose a differentiable vertex fitting algorithm that can be used for secondary vertex fitting, and that can be seamlessly integrated into neural networks for jet flavour tagging. Vertex fitting is formulated as an optimization problem where gradients of the optimized solution vertex are defined through implicit differentiation and can be passed to upstream or downstream neural network components for network training. More broadly, this is an application of differentiable programming to integrate physics knowledge into neural network models in high energy physics. We demonstrate how differentiable secondary vertex fitting can be integrated into larger transformer-based models for flavour tagging and improve heavy flavour jet classification.

Existing analyses of neural network training often operate under the unrealistic assumption of an extremely small learning rate. This lies in stark contrast to practical wisdom and empirical studies, such as the work of J. Cohen et al. (ICLR 2021), which exhibit startling new phenomena (the "edge of stability" or "unstable convergence") and potential benefits for generalization in the large learning rate regime. Despite a flurry of recent works on this topic, however, the latter effect is still poorly understood. In this paper, we take a step towards understanding genuinely non-convex training dynamics with large learning rates by performing a detailed analysis of gradient descent for simplified models of two-layer neural networks. For these models, we provably establish the edge of stability phenomenon and discover a sharp phase transition for the step size below which the neural network fails to learn "threshold-like" neurons (i.e., neurons with a non-zero first-layer bias). This elucidates one possible mechanism by which the edge of stability can in fact lead to better generalization, as threshold neurons are basic building blocks with useful inductive bias for many tasks.

Great progress has been made in automatic medical image segmentation due to powerful deep representation learning. The influence of transformer has led to research into its variants, and large-scale replacement of traditional CNN modules. However, such trend often overlooks the intrinsic feature extraction capabilities of the transformer and potential refinements to both the model and the transformer module through minor adjustments. This study proposes a novel deep medical image segmentation framework, called DA-TransUNet, aiming to introduce the Transformer and dual attention block into the encoder and decoder of the traditional U-shaped architecture. Unlike prior transformer-based solutions, our DA-TransUNet utilizes attention mechanism of transformer and multifaceted feature extraction of DA-Block, which can efficiently combine global, local, and multi-scale features to enhance medical image segmentation. Meanwhile, experimental results show that a dual attention block is added before the Transformer layer to facilitate feature extraction in the U-net structure. Furthermore, incorporating dual attention blocks in skip connections can enhance feature transfer to the decoder, thereby improving image segmentation performance. Experimental results across various benchmark of medical image segmentation reveal that DA-TransUNet significantly outperforms the state-of-the-art methods. The codes and parameters of our model will be publicly available at https://github.com/SUN-1024/DA-TransUnet.

Manifold learning flows are a class of generative modelling techniques that assume a low-dimensional manifold description of the data. The embedding of such manifold into the high-dimensional space of the data is achieved via learnable invertible transformations. Therefore, once the manifold is properly aligned via a reconstruction loss, the probability density is tractable on the manifold and maximum likelihood can be used optimize the network parameters. Naturally, the lower-dimensional representation of the data requires an injective-mapping. Recent approaches were able to enforce that density aligns with the modelled manifold, while efficiently calculating the density volume-change term when embedding to the higher-dimensional space. However, unless the injective-mapping is analytically predefined, the learned manifold is not necessarily an efficient representation of the data. Namely, the latent dimensions of such models frequently learn an entangled intrinsic basis with degenerate information being stored in each dimension. Alternatively, if a locally orthogonal and/or sparse basis is to be learned, here coined canonical intrinsic basis, it can serve in learning a more compact latent space representation. Towards this end, we propose a canonical manifold learning flow method, where a novel optimization objective enforces the transformation matrix to have few prominent and orthogonal basis functions. Canonical manifold flow yields a more efficient use of the latent space, automatically generating fewer prominent and distinct dimensions to represent data, and consequently a better approximation of target distributions than other manifold flow methods in most experiments we conducted, resulting in lower FID scores.

We propose the application of Transformer-based language models for classifying entity legal forms from raw legal entity names. Specifically, we employ various BERT variants and compare their performance against multiple traditional baselines. Our evaluation encompasses a substantial subset of freely available Legal Entity Identifier (LEI) data, comprising over 1.1 million legal entities from 30 different legal jurisdictions. The ground truth labels for classification per jurisdiction are taken from the Entity Legal Form (ELF) code standard (ISO 20275). Our findings demonstrate that pre-trained BERT variants outperform traditional text classification approaches in terms of F1 score, while also performing comparably well in the Macro F1 Score. Moreover, the validity of our proposal is supported by the outcome of third-party expert reviews conducted in ten selected jurisdictions. This study highlights the significant potential of Transformer-based models in advancing data standardization and data integration. The presented approaches can greatly benefit financial institutions, corporations, governments and other organizations in assessing business relationships, understanding risk exposure, and promoting effective governance.

We initiate a novel approach to explain the out of sample performance of random forest (RF) models by exploiting the fact that any RF can be formulated as an adaptive weighted K nearest-neighbors model. Specifically, we use the proximity between points in the feature space learned by the RF to re-write random forest predictions exactly as a weighted average of the target labels of training data points. This linearity facilitates a local notion of explainability of RF predictions that generates attributions for any model prediction across observations in the training set, and thereby complements established methods like SHAP, which instead generates attributions for a model prediction across dimensions of the feature space. We demonstrate this approach in the context of a bond pricing model trained on US corporate bond trades, and compare our approach to various existing approaches to model explainability.

This position paper explores the broad landscape of AI potentiality in the context of cybersecurity, with a particular emphasis on its possible risk factors with awareness, which can be managed by incorporating human experts in the loop, i.e., "Human-AI" teaming. As artificial intelligence (AI) technologies advance, they will provide unparalleled opportunities for attack identification, incident response, and recovery. However, the successful deployment of AI into cybersecurity measures necessitates an in-depth understanding of its capabilities, challenges, and ethical and legal implications to handle associated risk factors in real-world application areas. Towards this, we emphasize the importance of a balanced approach that incorporates AI's computational power with human expertise. AI systems may proactively discover vulnerabilities and detect anomalies through pattern recognition, and predictive modeling, significantly enhancing speed and accuracy. Human experts can explain AI-generated decisions to stakeholders, regulators, and end-users in critical situations, ensuring responsibility and accountability, which helps establish trust in AI-driven security solutions. Therefore, in this position paper, we argue that human-AI teaming is worthwhile in cybersecurity, in which human expertise such as intuition, critical thinking, or contextual understanding is combined with AI's computational power to improve overall cyber defenses.

Electric consumption prediction methods are investigated for many reasons such as decision-making related to energy efficiency as well as for anticipating demand in the energy market dynamics. The objective of the present work is the comparison between two Deep Learning models, namely the Long Short-Term Memory (LSTM) and Bi-directional LSTM (BLSTM) for univariate electric consumption Time Series (TS) short-term forecast. The Data Sets (DSs) were selected for their different contexts and scales, aiming the assessment of the models' robustness. Four DSs were used, related to the power consumption of: (a) a household in France; (b) a university building in Santar\'em, Brazil; (c) the T\'etouan city zones, in Morocco; and (c) the Singapore aggregated electric demand. The metrics RMSE, MAE, MAPE and R2 were calculated in a TS cross-validation scheme. The Friedman's test was applied to normalized RMSE (NRMSE) results, showing that BLSTM outperforms LSTM with statistically significant difference (p = 0.0455), corroborating the fact that bidirectional weight updating improves significantly the LSTM performance concerning different scales of electric power consumption.

Creating music is iterative, requiring varied methods at each stage. However, existing AI music systems fall short in orchestrating multiple subsystems for diverse needs. To address this gap, we introduce Loop Copilot, a novel system that enables users to generate and iteratively refine music through an interactive, multi-round dialogue interface. The system uses a large language model to interpret user intentions and select appropriate AI models for task execution. Each backend model is specialized for a specific task, and their outputs are aggregated to meet the user's requirements. To ensure musical coherence, essential attributes are maintained in a centralized table. We evaluate the effectiveness of the proposed system through semi-structured interviews and questionnaires, highlighting its utility not only in facilitating music creation but also its potential for broader applications.

This work reveals an evidential signal that emerges from the uncertainty value in Evidential Deep Learning (EDL). EDL is one example of a class of uncertainty-aware deep learning approaches designed to provide confidence (or epistemic uncertainty) about the current test sample. In particular for computer vision and bidirectional encoder large language models, the `evidential signal' arising from the Dirichlet strength in EDL can, in some cases, discriminate between classes, which is particularly strong when using large language models. We hypothesise that the KL regularisation term causes EDL to couple aleatoric and epistemic uncertainty. In this paper, we empirically investigate the correlations between misclassification and evaluated uncertainty, and show that EDL's `evidential signal' is due to misclassification bias. We critically evaluate EDL with other Dirichlet-based approaches, namely Generative Evidential Neural Networks (EDL-GEN) and Prior Networks, and show theoretically and empirically the differences between these loss functions. We conclude that EDL's coupling of uncertainty arises from these differences due to the use (or lack) of out-of-distribution samples during training.

Reasoning on knowledge graphs is a challenging task because it utilizes observed information to predict the missing one. Particularly, answering complex queries based on first-order logic is one of the crucial tasks to verify learning to reason abilities for generalization and composition. Recently, the prevailing method is query embedding which learns the embedding of a set of entities and treats logic operations as set operations and has shown great empirical success. Though there has been much research following the same formulation, many of its claims lack a formal and systematic inspection. In this paper, we rethink this formulation and justify many of the previous claims by characterizing the scope of queries investigated previously and precisely identifying the gap between its formulation and its goal, as well as providing complexity analysis for the currently investigated queries. Moreover, we develop a new dataset containing ten new types of queries with features that have never been considered and therefore can provide a thorough investigation of complex queries. Finally, we propose a new neural-symbolic method, Fuzzy Inference with Truth value (FIT), where we equip the neural link predictors with fuzzy logic theory to support end-to-end learning using complex queries with provable reasoning capability. Empirical results show that our method outperforms previous methods significantly in the new dataset and also surpasses previous methods in the existing dataset at the same time.

We present a Multimodal Interlaced Transformer (MIT) that jointly considers 2D and 3D data for weakly supervised point cloud segmentation. Research studies have shown that 2D and 3D features are complementary for point cloud segmentation. However, existing methods require extra 2D annotations to achieve 2D-3D information fusion. Considering the high annotation cost of point clouds, effective 2D and 3D feature fusion based on weakly supervised learning is in great demand. To this end, we propose a transformer model with two encoders and one decoder for weakly supervised point cloud segmentation using only scene-level class tags. Specifically, the two encoders compute the self-attended features for 3D point clouds and 2D multi-view images, respectively. The decoder implements interlaced 2D-3D cross-attention and carries out implicit 2D and 3D feature fusion. We alternately switch the roles of queries and key-value pairs in the decoder layers. It turns out that the 2D and 3D features are iteratively enriched by each other. Experiments show that it performs favorably against existing weakly supervised point cloud segmentation methods by a large margin on the S3DIS and ScanNet benchmarks. The project page will be available at https://jimmy15923.github.io/mit_web/.

Models trained on different datasets can be merged by a weighted-averaging of their parameters, but why does it work and when can it fail? Here, we connect the inaccuracy of weighted-averaging to mismatches in the gradients and propose a new uncertainty-based scheme to improve the performance by reducing the mismatch. The connection also reveals implicit assumptions in other schemes such as averaging, task arithmetic, and Fisher-weighted averaging. Our new method gives consistent improvements for large language models and vision transformers, both in terms of performance and robustness to hyperparameters.

Recent Language Models (LMs) have shown impressive capabilities in generating texts with the knowledge internalized in parameters. Yet, LMs often generate the factually incorrect responses to the given queries, since their knowledge may be inaccurate, incomplete, and outdated. To address this problem, previous works propose to augment LMs with the knowledge retrieved from an external knowledge source. However, such approaches often show suboptimal text generation performance due to two reasons: 1) the model may fail to retrieve the knowledge relevant to the given query, or 2) the model may not faithfully reflect the retrieved knowledge in the generated text. To overcome these, we propose to verify the output and the knowledge of the knowledge-augmented LMs with a separate verifier, which is a small LM that is trained to detect those two types of errors through instruction-finetuning. Then, when the verifier recognizes an error, we can rectify it by either retrieving new knowledge or generating new text. Further, we use an ensemble of the outputs from different instructions with a single verifier to enhance the reliability of the verification processes. We validate the effectiveness of the proposed verification steps on multiple question answering benchmarks, whose results show that the proposed verifier effectively identifies retrieval and generation errors, allowing LMs to provide more factually correct outputs. Our code is available at https://github.com/JinheonBaek/KALMV.

Open large language models (LLMs) with great performance in various tasks have significantly advanced the development of LLMs. However, they are far inferior to commercial models such as ChatGPT and GPT-4 when acting as agents to tackle complex tasks in the real world. These agent tasks employ LLMs as the central controller responsible for planning, memorization, and tool utilization, necessitating both fine-grained prompting methods and robust LLMs to achieve satisfactory performance. Though many prompting methods have been proposed to complete particular agent tasks, there is lack of research focusing on improving the agent capabilities of LLMs themselves without compromising their general abilities. In this work, we present AgentTuning, a simple and general method to enhance the agent abilities of LLMs while maintaining their general LLM capabilities. We construct AgentInstruct, a lightweight instruction-tuning dataset containing high-quality interaction trajectories. We employ a hybrid instruction-tuning strategy by combining AgentInstruct with open-source instructions from general domains. AgentTuning is used to instruction-tune the Llama 2 series, resulting in AgentLM. Our evaluations show that AgentTuning enables LLMs' agent capabilities without compromising general abilities. The AgentLM-70B is comparable to GPT-3.5-turbo on unseen agent tasks, demonstrating generalized agent capabilities. We open source the AgentInstruct and AgentLM-7B, 13B, and 70B models at https://github.com/THUDM/AgentTuning , serving open and powerful alternatives to commercial LLMs for agent tasks.

In light of the vulnerability of deep learning models to adversarial samples and the ensuing security issues, a range of methods, including Adversarial Training (AT) as a prominent representative, aimed at enhancing model robustness against various adversarial attacks, have seen rapid development. However, existing methods essentially assist the current state of target model to defend against parameter-oriented adversarial attacks with explicit or implicit computation burdens, which also suffers from unstable convergence behavior due to inconsistency of optimization trajectories. Diverging from previous work, this paper reconsiders the update rule of target model and corresponding deficiency to defend based on its current state. By introducing the historical state of the target model as a proxy, which is endowed with much prior information for defense, we formulate a two-stage update rule, resulting in a general adversarial defense framework, which we refer to as `LAST' ({\bf L}earn from the P{\bf ast}). Besides, we devise a Self Distillation (SD) based defense objective to constrain the update process of the proxy model without the introduction of larger teacher models. Experimentally, we demonstrate consistent and significant performance enhancements by refining a series of single-step and multi-step AT methods (e.g., up to $\bf 9.2\%$ and $\bf 20.5\%$ improvement of Robust Accuracy (RA) on CIFAR10 and CIFAR100 datasets, respectively) across various datasets, backbones and attack modalities, and validate its ability to enhance training stability and ameliorate catastrophic overfitting issues meanwhile.

Ocean science, which delves into the oceans that are reservoirs of life and biodiversity, is of great significance given that oceans cover over 70% of our planet's surface. Recently, advances in Large Language Models (LLMs) have transformed the paradigm in science. Despite the success in other domains, current LLMs often fall short in catering to the needs of domain experts like oceanographers, and the potential of LLMs for ocean science is under-explored. The intrinsic reason may be the immense and intricate nature of ocean data as well as the necessity for higher granularity and richness in knowledge. To alleviate these issues, we introduce OceanGPT, the first-ever LLM in the ocean domain, which is expert in various ocean science tasks. We propose DoInstruct, a novel framework to automatically obtain a large volume of ocean domain instruction data, which generates instructions based on multi-agent collaboration. Additionally, we construct the first oceanography benchmark, OceanBench, to evaluate the capabilities of LLMs in the ocean domain. Though comprehensive experiments, OceanGPT not only shows a higher level of knowledge expertise for oceans science tasks but also gains preliminary embodied intelligence capabilities in ocean technology. Codes, data and checkpoints will soon be available at https://github.com/zjunlp/KnowLM.

The key challenge underlying machine learning is generalisation to new data. This work studies generalisation for datasets consisting of related tasks that may differ in causal mechanisms. For example, observational medical data for complex diseases suffers from heterogeneity in causal mechanisms of disease across patients, creating challenges for machine learning algorithms that need to generalise to new patients outside of the training dataset. Common approaches for learning supervised models with heterogeneous datasets include learning a global model for the entire dataset, learning local models for each tasks' data, or utilising hierarchical, meta-learning and multi-task learning approaches to learn how to generalise from data pooled across multiple tasks. In this paper we propose causal similarity-based hierarchical Bayesian models to improve generalisation to new tasks by learning how to pool data from training tasks with similar causal mechanisms. We apply this general modelling principle to Bayesian neural networks and compare a variety of methods for estimating causal task similarity (for both known and unknown causal models). We demonstrate the benefits of our approach and applicability to real world problems through a range of experiments on simulated and real data.

In the realm of deep learning, transformers have emerged as a dominant architecture, particularly in natural language processing tasks. However, with their widespread adoption, concerns regarding the security and privacy of the data processed by these models have arisen. In this paper, we address a pivotal question: Can the data fed into transformers be recovered using their attention weights and outputs? We introduce a theoretical framework to tackle this problem. Specifically, we present an algorithm that aims to recover the input data $X \in \mathbb{R}^{d \times n}$ from given attention weights $W = QK^\top \in \mathbb{R}^{d \times d}$ and output $B \in \mathbb{R}^{n \times n}$ by minimizing the loss function $L(X)$. This loss function captures the discrepancy between the expected output and the actual output of the transformer. Our findings have significant implications for the Localized Layer-wise Mechanism (LLM), suggesting potential vulnerabilities in the model's design from a security and privacy perspective. This work underscores the importance of understanding and safeguarding the internal workings of transformers to ensure the confidentiality of processed data.

Many modern statistical analysis and machine learning applications require training models on sensitive user data. Differential privacy provides a formal guarantee that individual-level information about users does not leak. In this framework, randomized algorithms inject calibrated noise into the confidential data, resulting in privacy-protected datasets or queries. However, restricting access to only the privatized data during statistical analysis makes it computationally challenging to perform valid inferences on parameters underlying the confidential data. In this work, we propose simulation-based inference methods from privacy-protected datasets. Specifically, we use neural conditional density estimators as a flexible family of distributions to approximate the posterior distribution of model parameters given the observed private query results. We illustrate our methods on discrete time-series data under an infectious disease model and on ordinary linear regression models. Illustrating the privacy-utility trade-off, our experiments and analysis demonstrate the necessity and feasibility of designing valid statistical inference procedures to correct for biases introduced by the privacy-protection mechanisms.

Parameter-shared pre-trained language models (PLMs) have emerged as a successful approach in resource-constrained environments, enabling substantial reductions in model storage and memory costs without significant performance compromise. However, it is important to note that parameter sharing does not alleviate computational burdens associated with inference, thus impeding its practicality in situations characterized by limited stringent latency requirements or computational resources. Building upon neural ordinary differential equations (ODEs), we introduce a straightforward technique to enhance the inference efficiency of parameter-shared PLMs. Additionally, we propose a simple pre-training technique that leads to fully or partially shared models capable of achieving even greater inference acceleration. The experimental results demonstrate the effectiveness of our methods on both autoregressive and autoencoding PLMs, providing novel insights into more efficient utilization of parameter-shared models in resource-constrained settings.

Recent discoveries have revealed that deep neural networks might behave in a biased manner in many real-world scenarios. For instance, deep networks trained on a large-scale face recognition dataset CelebA tend to predict blonde hair for females and black hair for males. Such biases not only jeopardize the robustness of models but also perpetuate and amplify social biases, which is especially concerning for automated decision-making processes in healthcare, recruitment, etc., as they could exacerbate unfair economic and social inequalities among different groups. Existing debiasing methods suffer from high costs in bias labeling or model re-training, while also exhibiting a deficiency in terms of elucidating the origins of biases within the model. To this respect, we propose a fast model debiasing framework (FMD) which offers an efficient approach to identify, evaluate and remove biases inherent in trained models. The FMD identifies biased attributes through an explicit counterfactual concept and quantifies the influence of data samples with influence functions. Moreover, we design a machine unlearning-based strategy to efficiently and effectively remove the bias in a trained model with a small counterfactual dataset. Experiments on the Colored MNIST, CelebA, and Adult Income datasets along with experiments with large language models demonstrate that our method achieves superior or competing accuracies compared with state-of-the-art methods while attaining significantly fewer biases and requiring much less debiasing cost. Notably, our method requires only a small external dataset and updating a minimal amount of model parameters, without the requirement of access to training data that may be too large or unavailable in practice.

Metarounding is an approach to convert an approximation algorithm for linear optimization over some combinatorial classes to an online linear optimization algorithm for the same class. We propose a new metarounding algorithm under a natural assumption that a relax-based approximation algorithm exists for the combinatorial class. Our algorithm is much more efficient in both theoretical and practical aspects.

Prompt-based learning has shown its effectiveness in few-shot text classification. One important factor in its success is a verbalizer, which translates output from a language model into a predicted class. Notably, the simplest and widely acknowledged verbalizer employs manual labels to represent the classes. However, manual selection does not guarantee the optimality of the selected words when conditioned on the chosen language model. Therefore, we propose Label-Aware Automatic Verbalizer (LAAV), effectively augmenting the manual labels to achieve better few-shot classification results. Specifically, we use the manual labels along with the conjunction "and" to induce the model to generate more effective words for the verbalizer. The experimental results on five datasets across five languages demonstrate that LAAV significantly outperforms existing verbalizers. Furthermore, our analysis reveals that LAAV suggests more relevant words compared to similar approaches, especially in mid-to-low resource languages.

Medical time series data are indispensable in healthcare, providing critical insights for disease diagnosis, treatment planning, and patient management. The exponential growth in data complexity, driven by advanced sensor technologies, has presented challenges related to data labeling. Self-supervised learning (SSL) has emerged as a transformative approach to address these challenges, eliminating the need for extensive human annotation. In this study, we introduce a novel framework for Medical Time Series Representation Learning, known as MTS-LOF. MTS-LOF leverages the strengths of contrastive learning and Masked Autoencoder (MAE) methods, offering a unique approach to representation learning for medical time series data. By combining these techniques, MTS-LOF enhances the potential of healthcare applications by providing more sophisticated, context-rich representations. Additionally, MTS-LOF employs a multi-masking strategy to facilitate occlusion-invariant feature learning. This approach allows the model to create multiple views of the data by masking portions of it. By minimizing the discrepancy between the representations of these masked patches and the fully visible patches, MTS-LOF learns to capture rich contextual information within medical time series datasets. The results of experiments conducted on diverse medical time series datasets demonstrate the superiority of MTS-LOF over other methods. These findings hold promise for significantly enhancing healthcare applications by improving representation learning. Furthermore, our work delves into the integration of joint-embedding SSL and MAE techniques, shedding light on the intricate interplay between temporal and structural dependencies in healthcare data. This understanding is crucial, as it allows us to grasp the complexities of healthcare data analysis.

Neural operators, as an efficient surrogate model for learning the solutions of PDEs, have received extensive attention in the field of scientific machine learning. Among them, attention-based neural operators have become one of the mainstreams in related research. However, existing approaches overfit the limited training data due to the considerable number of parameters in the attention mechanism. To address this, we develop an orthogonal attention based on the eigendecomposition of the kernel integral operator and the neural approximation of eigenfunctions. The orthogonalization naturally poses a proper regularization effect on the resulting neural operator, which aids in resisting overfitting and boosting generalization. Experiments on six standard neural operator benchmark datasets comprising both regular and irregular geometries show that our method can outperform competing baselines with decent margins.

Large language models (LLMs) are susceptible to red teaming attacks, which can induce LLMs to generate harmful content. Previous research constructs attack prompts via manual or automatic methods, which have their own limitations on construction cost and quality. To address these issues, we propose an integrated approach that combines manual and automatic methods to economically generate high-quality attack prompts. Specifically, considering the impressive capabilities of newly emerged LLMs, we propose an attack framework to instruct LLMs to mimic human-generated prompts through in-context learning. Furthermore, we propose a defense framework that fine-tunes victim LLMs through iterative interactions with the attack framework to enhance their safety against red teaming attacks. Extensive experiments on different LLMs validate the effectiveness of our proposed attack and defense frameworks. Additionally, we release a series of attack prompts datasets named SAP with varying sizes, facilitating the safety evaluation and enhancement of more LLMs. Our code and dataset is available on https://github.com/Aatrox103/SAP .

Inference on modern Bayesian Neural Networks (BNNs) often relies on a variational inference treatment, imposing violated assumptions of independence and the form of the posterior. Traditional MCMC approaches avoid these assumptions at the cost of increased computation due to its incompatibility to subsampling of the likelihood. New Piecewise Deterministic Markov Process (PDMP) samplers permit subsampling, though introduce a model specific inhomogenous Poisson Process (IPPs) which is difficult to sample from. This work introduces a new generic and adaptive thinning scheme for sampling from these IPPs, and demonstrates how this approach can accelerate the application of PDMPs for inference in BNNs. Experimentation illustrates how inference with these methods is computationally feasible, can improve predictive accuracy, MCMC mixing performance, and provide informative uncertainty measurements when compared against other approximate inference schemes.

Clustering is an unsupervised machine learning methodology where unlabeled elements/objects are grouped together aiming to the construction of well-established clusters that their elements are classified according to their similarity. The goal of this process is to provide a useful aid to the researcher that will help her/him to identify patterns among the data. Dealing with large databases, such patterns may not be easily detectable without the contribution of a clustering algorithm. This article provides a deep description of the most widely used clustering methodologies accompanied by useful presentations concerning suitable parameter selection and initializations. Simultaneously, this article not only represents a review highlighting the major elements of examined clustering techniques but emphasizes the comparison of these algorithms' clustering efficiency based on 3 datasets, revealing their existing weaknesses and capabilities through accuracy and complexity, during the confrontation of discrete and continuous observations. The produced results help us extract valuable conclusions about the appropriateness of the examined clustering techniques in accordance with the dataset's size.

Existing works have made great progress in improving adversarial robustness, but typically test their method only on data from the same distribution as the training data, i.e. in-distribution (ID) testing. As a result, it is unclear how such robustness generalizes under input distribution shifts, i.e. out-of-distribution (OOD) testing. This is a concerning omission as such distribution shifts are unavoidable when methods are deployed in the wild. To address this issue we propose a benchmark named OODRobustBench to comprehensively assess OOD adversarial robustness using 23 dataset-wise shifts (i.e. naturalistic shifts in input distribution) and 6 threat-wise shifts (i.e., unforeseen adversarial threat models). OODRobustBench is used to assess 706 robust models using 60.7K adversarial evaluations. This large-scale analysis shows that: 1) adversarial robustness suffers from a severe OOD generalization issue; 2) ID robustness correlates strongly with OOD robustness, in a positive linear way, under many distribution shifts. The latter enables the prediction of OOD robustness from ID robustness. Based on this, we are able to predict the upper limit of OOD robustness for existing robust training schemes. The results suggest that achieving OOD robustness requires designing novel methods beyond the conventional ones. Last, we discover that extra data, data augmentation, advanced model architectures and particular regularization approaches can improve OOD robustness. Noticeably, the discovered training schemes, compared to the baseline, exhibit dramatically higher robustness under threat shift while keeping high ID robustness, demonstrating new promising solutions for robustness against both multi-attack and unforeseen attacks.

Recently there has been a lot of interest in non-autoregressive (non-AR) models for speech synthesis, such as FastSpeech 2 and diffusion models. Unlike AR models, these models do not have autoregressive dependencies among outputs which makes inference efficient. This paper expands the range of available non-AR models with another member called energy-based models (EBMs). The paper describes how noise contrastive estimation, which relies on the comparison between positive and negative samples, can be used to train EBMs. It proposes a number of strategies for generating effective negative samples, including using high-performing AR models. It also describes how sampling from EBMs can be performed using Langevin Markov Chain Monte-Carlo (MCMC). The use of Langevin MCMC enables to draw connections between EBMs and currently popular diffusion models. Experiments on LJSpeech dataset show that the proposed approach offers improvements over Tacotron 2.

Machine learning has rapidly become a tool of choice for the astronomical community. It is being applied across a wide range of wavelengths and problems, from the classification of transients to neural network emulators of cosmological simulations, and is shifting paradigms about how we generate and report scientific results. At the same time, this class of method comes with its own set of best practices, challenges, and drawbacks, which, at present, are often reported on incompletely in the astrophysical literature. With this paper, we aim to provide a primer to the astronomical community, including authors, reviewers, and editors, on how to implement machine learning models and report their results in a way that ensures the accuracy of the results, reproducibility of the findings, and usefulness of the method.

Despite the known limitations, most machine translation systems today still operate on the sentence-level. One reason for this is, that most parallel training data is only sentence-level aligned, without document-level meta information available. In this work, we set out to build context-aware translation systems utilizing document-level monolingual data instead. This can be achieved by combining any existing sentence-level translation model with a document-level language model. We improve existing approaches by leveraging recent advancements in model combination. Additionally, we propose novel weighting techniques that make the system combination more flexible and significantly reduce computational overhead. In a comprehensive evaluation on four diverse translation tasks, we show that our extensions improve document-targeted scores substantially and are also computationally more efficient. However, we also find that in most scenarios, back-translation gives even better results, at the cost of having to re-train the translation system. Finally, we explore language model fusion in the light of recent advancements in large language models. Our findings suggest that there might be strong potential in utilizing large language models via model combination.  ( 2 min )

SCGAN adds a similarity constraint between generated images and conditions as a regularization term on generative adversarial networks. Similarity constraint works as a tutor to instruct the generator network to comprehend the difference of representations based on conditions. We understand how SCGAN works on a deeper level. This understanding makes us realize that the similarity constraint functions like the contrastive loss function. We believe that a model with high understanding and intelligence measures the similarity between images based on their structure and high level features, just like humans do. Two major changes we applied to SCGAN in order to make a modified model are using SSIM to measure similarity between images and applying contrastive loss principles to the similarity constraint. The modified model performs better using FID and FactorVAE metrics. The modified model also has better generalisability compared to other models. Keywords Generative Adversarial Nets, Unsupervised Learning, Disentangled Representation Learning, Contrastive Disentanglement, SSIM  ( 2 min )

Options, serving as a crucial financial instrument, are used by investors to manage and mitigate their investment risks within the securities market. Precisely predicting the present price of an option enables investors to make informed and efficient decisions. In this paper, we propose a machine learning method for forecasting the prices of SPY (ETF) option based on gated recurrent unit (GRU) and self-attention mechanism. We first partitioned the raw dataset into 15 subsets according to moneyness and days to maturity criteria. For each subset, we matched the corresponding U.S. government bond rates and Implied Volatility Indices. This segmentation allows for a more insightful exploration of the impacts of risk-free rates and underlying volatility on option pricing. Next, we built four different machine learning models, including multilayer perceptron (MLP), long short-term memory (LSTM), self-attention LSTM, and self-attention GRU in comparison to the traditional binomial model. The empirical result shows that self-attention GRU with historical data outperforms other models due to its ability to capture complex temporal dependencies and leverage the contextual information embedded in the historical data. Finally, in order to unveil the "black box" of artificial intelligence, we employed the SHapley Additive exPlanations (SHAP) method to interpret and analyze the prediction results of the self-attention GRU model with historical data. This provides insights into the significance and contributions of different input features on the pricing of American-style options.  ( 2 min )

Randomized experimental comparisons of alternative pedagogical strategies could provide useful empirical evidence in instructors' decision-making. However, traditional experiments do not have a clear and simple pathway to using data rapidly to try to increase the chances that students in an experiment get the best conditions. Drawing inspiration from the use of machine learning and experimentation in product development at leading technology companies, we explore how adaptive experimentation might help in continuous course improvement. In adaptive experiments, as different arms/conditions are deployed to students, data is analyzed and used to change the experience for future students. This can be done using machine learning algorithms to identify which actions are more promising for improving student experience or outcomes. This algorithm can then dynamically deploy the most effective conditions to future students, resulting in better support for students' needs. We illustrate the approach with a case study providing a side-by-side comparison of traditional and adaptive experimentation of self-explanation prompts in online homework problems in a CS1 course. This provides a first step in exploring the future of how this methodology can be useful in bridging research and practice in doing continuous improvement.  ( 2 min )

Matching, a task to optimally assign limited resources under constraints, is a fundamental technology for society. The task potentially has various objectives, conditions, and constraints; however, the efficient neural network architecture for matching is underexplored. This paper proposes a novel graph neural network (GNN), \textit{WeaveNet}, designed for bipartite graphs. Since a bipartite graph is generally dense, general GNN architectures lose node-wise information by over-smoothing when deeply stacked. Such a phenomenon is undesirable for solving matching problems. WeaveNet avoids it by preserving edge-wise information while passing messages densely to reach a better solution. To evaluate the model, we approximated one of the \textit{strongly NP-hard} problems, \textit{fair stable matching}. Despite its inherent difficulties and the network's general purpose design, our model reached a comparative performance with state-of-the-art algorithms specially designed for stable matching for small numbers of agents.  ( 2 min )

Graph Neural Networks (GNNs) have shown remarkable merit in performing various learning-based tasks in complex networks. The superior performance of GNNs often correlates with the availability and quality of node-level features in the input networks. However, for many network applications, such node-level information may be missing or unreliable, thereby limiting the applicability and efficacy of GNNs. To address this limitation, we present a novel approach denoted as Ego-centric Spectral subGraph Embedding Augmentation (ESGEA), which aims to enhance and design node features, particularly in scenarios where information is lacking. Our method leverages the topological structure of the local subgraph to create topology-aware node features. The subgraph features are generated using an efficient spectral graph embedding technique, and they serve as node features that capture the local topological organization of the network. The explicit node features, if present, are then enhanced with the subgraph embeddings in order to improve the overall performance. ESGEA is compatible with any GNN-based architecture and is effective even in the absence of node features. We evaluate the proposed method in a social network graph classification task where node attributes are unavailable, as well as in a node classification task where node features are corrupted or even absent. The evaluation results on seven datasets and eight baseline models indicate up to a 10% improvement in AUC and a 7% improvement in accuracy for graph and node classification tasks, respectively.  ( 3 min )

Graph Neural Networks (GNNs) have been widely used for various types of graph data processing and analytical tasks in different domains. Training GNNs over centralized graph data can be infeasible due to privacy concerns and regulatory restrictions. Thus, federated learning (FL) becomes a trending solution to address this challenge in a distributed learning paradigm. However, as GNNs may inherit historical bias from training data and lead to discriminatory predictions, the bias of local models can be easily propagated to the global model in distributed settings. This poses a new challenge in mitigating bias in federated GNNs. To address this challenge, we propose $\text{F}^2$GNN, a Fair Federated Graph Neural Network, that enhances group fairness of federated GNNs. As bias can be sourced from both data and learning algorithms, $\text{F}^2$GNN aims to mitigate both types of bias under federated settings. First, we provide theoretical insights on the connection between data bias in a training graph and statistical fairness metrics of the trained GNN models. Based on the theoretical analysis, we design $\text{F}^2$GNN which contains two key components: a fairness-aware local model update scheme that enhances group fairness of the local models on the client side, and a fairness-weighted global model update scheme that takes both data bias and fairness metrics of local models into consideration in the aggregation process. We evaluate $\text{F}^2$GNN empirically versus a number of baseline methods, and demonstrate that $\text{F}^2$GNN outperforms these baselines in terms of both fairness and model accuracy.  ( 3 min )

Consider the following problem: given a few demonstrations of a task across a few different objects, how can a robot learn to perform that same task on new, previously unseen objects? This is challenging because the large variety of objects within a class makes it difficult to infer the task-relevant relationship between the new objects and the objects in the demonstrations. We address this by formulating imitation learning as a conditional alignment problem between graph representations of objects. Consequently, we show that this conditioning allows for in-context learning, where a robot can perform a task on a set of new objects immediately after the demonstrations, without any prior knowledge about the object class or any further training. In our experiments, we explore and validate our design choices, and we show that our method is highly effective for few-shot learning of several real-world, everyday tasks, whilst outperforming baselines. Videos are available on our project webpage at https://www.robot-learning.uk/implicit-graph-alignment.  ( 2 min )

Deep Learning models have shown remarkable performance in a broad range of vision tasks. However, they are often vulnerable against domain shifts at test-time. Test-time training (TTT) methods have been developed in an attempt to mitigate these vulnerabilities, where a secondary task is solved at training time simultaneously with the main task, to be later used as an self-supervised proxy task at test-time. In this work, we propose a novel unsupervised TTT technique based on the maximization of Mutual Information between multi-scale feature maps and a discrete latent representation, which can be integrated to the standard training as an auxiliary clustering task. Experimental results demonstrate competitive classification performance on different popular test-time adaptation benchmarks.  ( 2 min )

The optimal placement of sensors for environmental monitoring and disaster management is a challenging problem due to its NP-hard nature. Traditional methods for sensor placement involve exact, approximation, or heuristic approaches, with the latter being the most widely used. However, heuristic methods are limited by expert intuition and experience. Deep learning (DL) has emerged as a promising approach for generating heuristic algorithms automatically. In this paper, we introduce a novel sensor placement approach focused on learning improvement heuristics using deep reinforcement learning (RL) methods. Our approach leverages an RL formulation for learning improvement heuristics, driven by an actor-critic algorithm for training the policy network. We compare our method with several state-of-the-art approaches by conducting comprehensive experiments, demonstrating the effectiveness and superiority of our proposed approach in producing high-quality solutions. Our work presents a promising direction for applying advanced DL and RL techniques to challenging climate sensor placement problems.  ( 2 min )

In recent years, Massive Open Online Courses (MOOCs) have gained significant traction as a rapidly growing phenomenon in online learning. Unlike traditional classrooms, MOOCs offer a unique opportunity to cater to a diverse audience from different backgrounds and geographical locations. Renowned universities and MOOC-specific providers, such as Coursera, offer MOOC courses on various subjects. Automated assessment tasks like grade and early dropout predictions are necessary due to the high enrollment and limited direct interaction between teachers and learners. However, current automated assessment approaches overlook the structural links between different entities involved in the downstream tasks, such as the students and courses. Our hypothesis suggests that these structural relationships, manifested through an interaction graph, contain valuable information that can enhance the performance of the task at hand. To validate this, we construct a unique knowledge graph for a large MOOC dataset, which will be publicly available to the research community. Furthermore, we utilize graph embedding techniques to extract latent structural information encoded in the interactions between entities in the dataset. These techniques do not require ground truth labels and can be utilized for various tasks. Finally, by combining entity-specific features, behavioral features, and extracted structural features, we enhance the performance of predictive machine learning models in student assignment grade prediction. Our experiments demonstrate that structural features can significantly improve the predictive performance of downstream assessment tasks. The code and data are available in \url{https://github.com/DSAatUSU/MOOPer_grade_prediction}  ( 3 min )

Variable speed limit (VSL) control is a promising traffic management strategy for enhancing safety and mobility. This work introduces MARVEL, a multi-agent reinforcement learning (MARL) framework for implementing large-scale VSL control on freeway corridors using only commonly available data. The agents learn through a reward structure that incorporates adaptability to traffic conditions, safety, and mobility; enabling coordination among the agents. The proposed framework scales to cover corridors with many gantries thanks to a parameter sharing among all VSL agents. The agents are trained in a microsimulation environment based on a short freeway stretch with 8 gantries spanning 7 miles and tested with 34 gantries spanning 17 miles of I-24 near Nashville, TN. MARVEL improves traffic safety by 63.4% compared to the no control scenario and enhances traffic mobility by 14.6% compared to a state-of-the-practice algorithm that has been deployed on I-24. An explainability analysis is undertaken to explore the learned policy under different traffic conditions and the results provide insights into the decision-making process of agents. Finally, we test the policy learned from the simulation-based experiments on real input data from I-24 to illustrate the potential deployment capability of the learned policy.  ( 2 min )

Graph neural networks (GNNs) have gained significant popularity due to the powerful capability to extract useful representations from graph data. As the need for efficient GNN computation intensifies, a variety of programming abstractions designed for optimizing GNN Aggregation have emerged to facilitate acceleration. However, there is no comprehensive evaluation and analysis upon existing abstractions, thus no clear consensus on which approach is better. In this letter, we classify existing programming abstractions for GNN Aggregation by the dimension of data organization and propagation method. By constructing these abstractions on a state-of-the-art GNN library, we perform a thorough and detailed characterization study to compare their performance and efficiency, and provide several insights on future GNN acceleration based on our analysis.  ( 2 min )

The performance of neural networks has been significantly improved by increasing the number of channels in convolutional layers. However, this increase in performance comes with a higher computational cost, resulting in numerous studies focused on reducing it. One promising approach to address this issue is group convolution, which effectively reduces the computational cost by grouping channels. However, to the best of our knowledge, there has been no theoretical analysis on how well the group convolution approximates the standard convolution. In this paper, we mathematically analyze the approximation of the group convolution to the standard convolution with respect to the number of groups. Furthermore, we propose a novel variant of the group convolution called balanced group convolution, which shows a higher approximation with a small additional computational cost. We provide experimental results that validate our theoretical findings and demonstrate the superior performance of the balanced group convolution over other variants of group convolution.  ( 2 min )

Recently, the field of machine learning has undergone a transition from model-centric to data-centric. The advancements in diverse learning tasks have been propelled by the accumulation of more extensive datasets, subsequently facilitating the training of larger models on these datasets. However, these datasets remain relatively under-explored. To this end, we introduce a pioneering approach known as RK-core, to empower gaining a deeper understanding of the intricate hierarchical structure within datasets. Across several benchmark datasets, we find that samples with low coreness values appear less representative of their respective categories, and conversely, those with high coreness values exhibit greater representativeness. Correspondingly, samples with high coreness values make a more substantial contribution to the performance in comparison to those with low coreness values. Building upon this, we further employ RK-core to analyze the hierarchical structure of samples with different coreset selection methods. Remarkably, we find that a high-quality coreset should exhibit hierarchical diversity instead of solely opting for representative samples. The code is available at https://github.com/yaolu-zjut/Kcore.  ( 2 min )

Numerous methods for time series anomaly detection (TSAD) methods have emerged in recent years. Most existing methods are unsupervised and assume the availability of normal training samples only, while few supervised methods have shown superior performance by incorporating labeled anomalous samples in the training phase. However, certain anomaly types are inherently challenging for unsupervised methods to differentiate from normal data, while supervised methods are constrained to detecting anomalies resembling those present during training, failing to generalize to unseen anomaly classes. This paper is the first attempt in providing a novel approach for the open-set TSAD problem, in which a small number of labeled anomalies from a limited class of anomalies are visible in the training phase, with the objective of detecting both seen and unseen anomaly classes in the test phase. The proposed method, called Multivariate Open-Set timeseries Anomaly Detection (MOSAD) consists of three primary modules: a Feature Extractor to extract meaningful time-series features; a Multi-head Network consisting of Generative-, Deviation-, and Contrastive heads for capturing both seen and unseen anomaly classes; and an Anomaly Scoring module leveraging the insights of the three heads to detect anomalies. Extensive experiments on three real-world datasets consistently show that our approach surpasses existing methods under various experimental settings, thus establishing a new state-of-the-art performance in the TSAD field.  ( 2 min )

Molecular language modeling is an effective approach to generating novel chemical structures. However, these models do not \emph{a priori} encode certain preferences a chemist may desire. We investigate the use of fine-tuning using Direct Preference Optimization to better align generated molecules with chemist preferences. Our findings suggest that this approach is simple, efficient, and highly effective.  ( 2 min )

With evolving data regulations, machine unlearning (MU) has become an important tool for fostering trust and safety in today's AI models. However, existing MU methods focusing on data and/or weight perspectives often grapple with limitations in unlearning accuracy, stability, and cross-domain applicability. To address these challenges, we introduce the concept of 'weight saliency' in MU, drawing parallels with input saliency in model explanation. This innovation directs MU's attention toward specific model weights rather than the entire model, improving effectiveness and efficiency. The resultant method that we call saliency unlearning (SalUn) narrows the performance gap with 'exact' unlearning (model retraining from scratch after removing the forgetting dataset). To the best of our knowledge, SalUn is the first principled MU approach adaptable enough to effectively erase the influence of forgetting data, classes, or concepts in both image classification and generation. For example, SalUn yields a stability advantage in high-variance random data forgetting, e.g., with a 0.2% gap compared to exact unlearning on the CIFAR-10 dataset. Moreover, in preventing conditional diffusion models from generating harmful images, SalUn achieves nearly 100% unlearning accuracy, outperforming current state-of-the-art baselines like Erased Stable Diffusion and Forget-Me-Not.  ( 2 min )

Understanding the long-term impact of algorithmic interventions on society is vital to achieving responsible AI. Traditional evaluation strategies often fall short due to the complex, adaptive and dynamic nature of society. While reinforcement learning (RL) can be a powerful approach for optimizing decisions in dynamic settings, the difficulty of realistic environment design remains a barrier to building robust agents that perform well in practical settings. To address this issue we tap into the field of system dynamics (SD) as a complementary method that incorporates collaborative simulation model specification practices. We introduce SDGym, a low-code library built on the OpenAI Gym framework which enables the generation of custom RL environments based on SD simulation models. Through a feasibility study we validate that well specified, rich RL environments can be generated from preexisting SD models and a few lines of configuration code. We demonstrate the capabilities of the SDGym environment using an SD model of the electric vehicle adoption problem. We compare two SD simulators, PySD and BPTK-Py for parity, and train a D4PG agent using the Acme framework to showcase learning and environment interaction. Our preliminary findings underscore the dual potential of SD to improve RL environment design and for RL to improve dynamic policy discovery within SD models. By open-sourcing SDGym, the intent is to galvanize further research and promote adoption across the SD and RL communities, thereby catalyzing collaboration in this emerging interdisciplinary space.  ( 2 min )

Despite their better convergence properties compared to first-order optimizers, second-order optimizers for deep learning have been less popular due to their significant computational costs. The primary efficiency bottleneck in such optimizers is matrix inverse calculations in the preconditioning step, which are expensive to compute on GPUs. In this paper, we introduce Jorge, a second-order optimizer that promises the best of both worlds -- rapid convergence benefits of second-order methods, and high computational efficiency typical of first-order methods. We address the primary computational bottleneck of computing matrix inverses by completely eliminating them using an approximation of the preconditioner computation. This makes Jorge extremely efficient on GPUs in terms of wall-clock time. Further, we describe an approach to determine Jorge's hyperparameters directly from a well-tuned SGD baseline, thereby significantly minimizing tuning efforts. Our empirical evaluations demonstrate the distinct advantages of using Jorge, outperforming state-of-the-art optimizers such as SGD, AdamW, and Shampoo across multiple deep learning models, both in terms of sample efficiency and wall-clock time.  ( 2 min )

Driving safety is a top priority for autonomous vehicles. Orthogonal to prior work handling accident-prone traffic events by algorithm designs at the policy level, we investigate a Closed-loop Adversarial Training (CAT) framework for safe end-to-end driving in this paper through the lens of environment augmentation. CAT aims to continuously improve the safety of driving agents by training the agent on safety-critical scenarios that are dynamically generated over time. A novel resampling technique is developed to turn log-replay real-world driving scenarios into safety-critical ones via probabilistic factorization, where the adversarial traffic generation is modeled as the multiplication of standard motion prediction sub-problems. Consequently, CAT can launch more efficient physical attacks compared to existing safety-critical scenario generation methods and yields a significantly less computational cost in the iterative learning pipeline. We incorporate CAT into the MetaDrive simulator and validate our approach on hundreds of driving scenarios imported from real-world driving datasets. Experimental results demonstrate that CAT can effectively generate adversarial scenarios countering the agent being trained. After training, the agent can achieve superior driving safety in both log-replay and safety-critical traffic scenarios on the held-out test set. Code and data are available at https://metadriverse.github.io/cat.  ( 2 min )

Supervised contrastive loss (SCL) is a competitive and often superior alternative to the cross-entropy loss for classification. While prior studies have demonstrated that both losses yield symmetric training representations under balanced data, this symmetry breaks under class imbalances. This paper presents an intriguing discovery: the introduction of a ReLU activation at the final layer effectively restores the symmetry in SCL-learned representations. We arrive at this finding analytically, by establishing that the global minimizers of an unconstrained features model with SCL loss and entry-wise non-negativity constraints form an orthogonal frame. Extensive experiments conducted across various datasets, architectures, and imbalance scenarios corroborate our finding. Importantly, our experiments reveal that the inclusion of the ReLU activation restores symmetry without compromising test accuracy. This constitutes the first geometry characterization of SCL under imbalances. Additionally, our analysis and experiments underscore the pivotal role of batch selection strategies in representation geometry. By proving necessary and sufficient conditions for mini-batch choices that ensure invariant symmetric representations, we introduce batch-binding as an efficient strategy that guarantees these conditions hold.  ( 2 min )

The dynamical formulation of the optimal transport can be extended through various choices of the underlying geometry ($\textit{kinetic energy}$), and the regularization of density paths ($\textit{potential energy}$). These combinations yield different variational problems ($\textit{Lagrangians}$), encompassing many variations of the optimal transport problem such as the Schr\"odinger bridge, unbalanced optimal transport, and optimal transport with physical constraints, among others. In general, the optimal density path is unknown, and solving these variational problems can be computationally challenging. Leveraging the dual formulation of the Lagrangians, we propose a novel deep learning based framework approaching all of these problems from a unified perspective. Our method does not require simulating or backpropagating through the trajectories of the learned dynamics, and does not need access to optimal couplings. We showcase the versatility of the proposed framework by outperforming previous approaches for the single-cell trajectory inference, where incorporating prior knowledge into the dynamics is crucial for correct predictions.  ( 2 min )

Clustering is an unsupervised machine learning methodology where unlabeled elements/objects are grouped together aiming to the construction of well-established clusters that their elements are classified according to their similarity. The goal of this process is to provide a useful aid to the researcher that will help her/him to identify patterns among the data. Dealing with large databases, such patterns may not be easily detectable without the contribution of a clustering algorithm. This article provides a deep description of the most widely used clustering methodologies accompanied by useful presentations concerning suitable parameter selection and initializations. Simultaneously, this article not only represents a review highlighting the major elements of examined clustering techniques but emphasizes the comparison of these algorithms' clustering efficiency based on 3 datasets, revealing their existing weaknesses and capabilities through accuracy and complexity, during the confrontation of discrete and continuous observations. The produced results help us extract valuable conclusions about the appropriateness of the examined clustering techniques in accordance with the dataset's size.  ( 3 min )

On-device machine learning (ML) enables the training process to exploit a massive amount of user-generated private data samples. To enjoy this benefit, inter-device communication overhead should be minimized. With this end, we propose federated distillation (FD), a distributed model training algorithm whose communication payload size is much smaller than a benchmark scheme, federated learning (FL), particularly when the model size is large. Moreover, user-generated data samples are likely to become non-IID across devices, which commonly degrades the performance compared to the case with an IID dataset. To cope with this, we propose federated augmentation (FAug), where each device collectively trains a generative model, and thereby augments its local data towards yielding an IID dataset. Empirical studies demonstrate that FD with FAug yields around 26x less communication overhead while achieving 95-98% test accuracy compared to FL.  ( 2 min )

Partially observable Markov decision processes (POMDPs) provide a flexible representation for real-world decision and control problems. However, POMDPs are notoriously difficult to solve, especially when the state and observation spaces are continuous or hybrid, which is often the case for physical systems. While recent online sampling-based POMDP algorithms that plan with observation likelihood weighting have shown practical effectiveness, a general theory characterizing the approximation error of the particle filtering techniques that these algorithms use has not previously been proposed. Our main contribution is bounding the error between any POMDP and its corresponding finite sample particle belief MDP (PB-MDP) approximation. This fundamental bridge between PB-MDPs and POMDPs allows us to adapt any sampling-based MDP algorithm to a POMDP by solving the corresponding particle belief MDP, thereby extending the convergence guarantees of the MDP algorithm to the POMDP. Practically, this is implemented by using the particle filter belief transition model as the generative model for the MDP solver. While this requires access to the observation density model from the POMDP, it only increases the transition sampling complexity of the MDP solver by a factor of $\mathcal{O}(C)$, where $C$ is the number of particles. Thus, when combined with sparse sampling MDP algorithms, this approach can yield algorithms for POMDPs that have no direct theoretical dependence on the size of the state and observation spaces. In addition to our theoretical contribution, we perform five numerical experiments on benchmark POMDPs to demonstrate that a simple MDP algorithm adapted using PB-MDP approximation, Sparse-PFT, achieves performance competitive with other leading continuous observation POMDP solvers.  ( 3 min )

We present a novel variational framework for performing inference in (neural) stochastic differential equations (SDEs) driven by Markov-approximate fractional Brownian motion (fBM). SDEs offer a versatile tool for modeling real-world continuous-time dynamic systems with inherent noise and randomness. Combining SDEs with the powerful inference capabilities of variational methods, enables the learning of representative function distributions through stochastic gradient descent. However, conventional SDEs typically assume the underlying noise to follow a Brownian motion (BM), which hinders their ability to capture long-term dependencies. In contrast, fractional Brownian motion (fBM) extends BM to encompass non-Markovian dynamics, but existing methods for inferring fBM parameters are either computationally demanding or statistically inefficient. In this paper, building upon the Markov approximation of fBM, we derive the evidence lower bound essential for efficient variational inference of posterior path measures, drawing from the well-established field of stochastic analysis. Additionally, we provide a closed-form expression to determine optimal approximation coefficients. Furthermore, we propose the use of neural networks to learn the drift, diffusion and control terms within our variational posterior, leading to the variational training of neural-SDEs. In this framework, we also optimize the Hurst index, governing the nature of our fractional noise. Beyond validation on synthetic data, we contribute a novel architecture for variational latent video prediction,-an approach that, to the best of our knowledge, enables the first variational neural-SDE application to video perception.  ( 3 min )

Feature preprocessing continues to play a critical role when applying machine learning and statistical methods to tabular data. In this paper, we propose the use of the kernel density integral transformation as a feature preprocessing step. Our approach subsumes the two leading feature preprocessing methods as limiting cases: linear min-max scaling and quantile transformation. We demonstrate that, without hyperparameter tuning, the kernel density integral transformation can be used as a simple drop-in replacement for either method, offering protection from the weaknesses of each. Alternatively, with tuning of a single continuous hyperparameter, we frequently outperform both of these methods. Finally, we show that the kernel density transformation can be profitably applied to statistical data analysis, particularly in correlation analysis and univariate clustering.  ( 2 min )

Prediction sets capture uncertainty by predicting sets of labels rather than individual labels, enabling downstream decisions to conservatively account for all plausible outcomes. Conformal inference algorithms construct prediction sets guaranteed to contain the true label with high probability. These guarantees fail to hold in the face of distribution shift, which is precisely when reliable uncertainty quantification can be most useful. We propose a novel algorithm for constructing prediction sets with PAC guarantees in the label shift setting. This method estimates the predicted probabilities of the classes in a target domain, as well as the confusion matrix, then propagates uncertainty in these estimates through a Gaussian elimination algorithm to compute confidence intervals for importance weights. Finally, it uses these intervals to construct prediction sets. We evaluate our approach on five datasets: the CIFAR-10, ChestX-Ray and Entity-13 image datasets, the tabular CDC Heart dataset, and the AGNews text dataset. Our algorithm satisfies the PAC guarantee while producing smaller, more informative, prediction sets compared to several baselines.  ( 2 min )

This paper introduces a new regularized version of the robust $\tau$-regression estimator for analyzing high-dimensional datasets subject to gross contamination in the response variables and covariates (explanatory variables). The resulting estimator, termed adaptive $\tau$-Lasso, is robust to outliers and high-leverage points. It also incorporates an adaptive $\ell_1$-norm penalty term, which enables the selection of relevant variables and reduces the bias associated with large true regression coefficients. More specifically, this adaptive $\ell_1$-norm penalty term assigns a weight to each regression coefficient. For a fixed number of predictors $p$, we show that the adaptive $\tau$-Lasso has the oracle property, ensuring both variable-selection consistency and asymptotic normality. Asymptotic normality applies only to the entries of the regression vector corresponding to the true support, assuming knowledge of the true regression vector support. We characterize its robustness via the finite-sample breakdown point and the influence function. We carry out extensive simulations and observe that the class of $\tau$-Lasso estimators exhibits robustness and reliable performance in both contaminated and uncontaminated data settings. We also validate our theoretical findings on robustness properties through simulation experiments. In the face of outliers and high-leverage points, the adaptive $\tau$-Lasso and $\tau$-Lasso estimators achieve the best performance or close-to-best performance in terms of prediction and variable selection accuracy compared to other competing regularized estimators for all scenarios considered in this study. Therefore, the adaptive $\tau$-Lasso and $\tau$-Lasso estimators can be effectively employed for a variety of sparse linear regression problems, particularly in high-dimensional settings and when the data is contaminated by outliers and high-leverage points.  ( 3 min )

Representation learning has significantly driven the field to develop pretrained models that can act as a valuable starting point when transferring to new datasets. With the rising demand for reliable machine learning and uncertainty quantification, there is a need for pretrained models that not only provide embeddings but also transferable uncertainty estimates. To guide the development of such models, we propose the Uncertainty-aware Representation Learning (URL) benchmark. Besides the transferability of the representations, it also measures the zero-shot transferability of the uncertainty estimate using a novel metric. We apply URL to evaluate eleven uncertainty quantifiers that are pretrained on ImageNet and transferred to eight downstream datasets. We find that approaches that focus on the uncertainty of the representation itself or estimate the prediction risk directly outperform those that are based on the probabilities of upstream classes. Yet, achieving transferable uncertainty quantification remains an open challenge. Our findings indicate that it is not necessarily in conflict with traditional representation learning goals. Code is provided under https://github.com/mkirchhof/url .  ( 2 min )

Gibbs posteriors are proportional to a prior distribution multiplied by an exponentiated loss function, with a key tuning parameter weighting information in the loss relative to the prior and providing a control of posterior uncertainty. Gibbs posteriors provide a principled framework for likelihood-free Bayesian inference, but in many situations, including a single tuning parameter inevitably leads to poor uncertainty quantification. In particular, regardless of the value of the parameter, credible regions have far from the nominal frequentist coverage even in large samples. We propose a sequential extension to Gibbs posteriors to address this problem. We prove the proposed sequential posterior exhibits concentration and a Bernstein-von Mises theorem, which holds under easy to verify conditions in Euclidean space and on manifolds. As a byproduct, we obtain the first Bernstein-von Mises theorem for traditional likelihood-based Bayesian posteriors on manifolds. All methods are illustrated with an application to principal component analysis.  ( 2 min )

This work introduces the first toolkit around path-norms that is fully able to encompass general DAG ReLU networks with biases, skip connections and any operation based on the extraction of order statistics: max pooling, GroupSort etc. This toolkit notably allows us to establish generalization bounds for modern neural networks that are not only the most widely applicable path-norm based ones, but also recover or beat the sharpest known bounds of this type. These extended path-norms further enjoy the usual benefits of path-norms: ease of computation, invariance under the symmetries of the network, and improved sharpness on feedforward networks compared to the product of operators' norms, another complexity measure most commonly used. The versatility of the toolkit and its ease of implementation allow us to challenge the concrete promises of path-norm-based generalization bounds, by numerically evaluating the sharpest known bounds for ResNets on ImageNet.  ( 2 min )

If machine learning models were to achieve superhuman abilities at various reasoning or decision-making tasks, how would we go about evaluating such models, given that humans would necessarily be poor proxies for ground truth? In this paper, we propose a framework for evaluating superhuman models via consistency checks. Our premise is that while the correctness of superhuman decisions may be impossible to evaluate, we can still surface mistakes if the model's decisions fail to satisfy certain logical, human-interpretable rules. We instantiate our framework on three tasks where correctness of decisions is hard to evaluate due to either superhuman model abilities, or to otherwise missing ground truth: evaluating chess positions, forecasting future events, and making legal judgments. We show that regardless of a model's (possibly superhuman) performance on these tasks, we can discover logical inconsistencies in decision making. For example: a chess engine assigning opposing valuations to semantically identical boards; GPT-4 forecasting that sports records will evolve non-monotonically over time; or an AI judge assigning bail to a defendant only after we add a felony to their criminal record.  ( 2 min )

In order to trust the predictions of a machine learning algorithm, it is necessary to understand the factors that contribute to those predictions. In the case of probabilistic and uncertainty-aware models, it is necessary to understand not only the reasons for the predictions themselves, but also the model's level of confidence in those predictions. In this paper, we show how existing methods in explainability can be extended to uncertainty-aware models and how such extensions can be used to understand the sources of uncertainty in a model's predictive distribution. In particular, by adapting permutation feature importance, partial dependence plots, and individual conditional expectation plots, we demonstrate that novel insights into model behaviour may be obtained and that these methods can be used to measure the impact of features on both the entropy of the predictive distribution and the log-likelihood of the ground truth labels under that distribution. With experiments using both synthetic and real-world data, we demonstrate the utility of these approaches in understanding both the sources of uncertainty and their impact on model performance.  ( 2 min )

A metric tensor for Riemann manifold Monte Carlo particularly suited for non-linear Bayesian hierarchical models is proposed. The metric tensor is built from symmetric positive semidefinite log-density gradient covariance (LGC) matrices, which are also proposed and further explored here. The LGCs generalize the Fisher information matrix by measuring the joint information content and dependence structure of both a random variable and the parameters of said variable. Consequently, positive definite Fisher/LGC-based metric tensors may be constructed not only from the observation likelihoods as is current practice, but also from arbitrarily complicated non-linear prior/latent variable structures, provided the LGC may be derived for each conditional distribution used to construct said structures. The proposed methodology is highly automatic and allows for exploitation of any sparsity associated with the model in question. When implemented in conjunction with a Riemann manifold variant of the recently proposed numerical generalized randomized Hamiltonian Monte Carlo processes, the proposed methodology is highly competitive, in particular for the more challenging target distributions associated with Bayesian hierarchical models.  ( 2 min )

Physics-informed neural networks have emerged as a coherent framework for building predictive models that combine statistical patterns with domain knowledge. The underlying notion is to enrich the optimization loss function with known relationships to constrain the space of possible solutions. Hydrodynamic simulations are a core constituent of modern cosmology, while the required computations are both expensive and time-consuming. At the same time, the comparatively fast simulation of dark matter requires fewer resources, which has led to the emergence of machine learning algorithms for baryon inpainting as an active area of research; here, recreating the scatter found in hydrodynamic simulations is an ongoing challenge. This paper presents the first application of physics-informed neural networks to baryon inpainting by combining advances in neural network architectures with physical constraints, injecting theory on baryon conversion efficiency into the model loss function. We also introduce a punitive prediction comparison based on the Kullback-Leibler divergence, which enforces scatter reproduction. By simultaneously extracting the complete set of baryonic properties for the Simba suite of cosmological simulations, our results demonstrate improved accuracy of baryonic predictions based on dark matter halo properties, successful recovery of the fundamental metallicity relation, and retrieve scatter that traces the target simulation's distribution.  ( 3 min )

Embodied agents operate in a structured world, often solving tasks with spatial, temporal, and permutation symmetries. Most algorithms for planning and model-based reinforcement learning (MBRL) do not take this rich geometric structure into account, leading to sample inefficiency and poor generalization. We introduce the Equivariant Diffuser for Generating Interactions (EDGI), an algorithm for MBRL and planning that is equivariant with respect to the product of the spatial symmetry group SE(3), the discrete-time translation group Z, and the object permutation group Sn. EDGI follows the Diffuser framework (Janner et al., 2022) in treating both learning a world model and planning in it as a conditional generative modeling problem, training a diffusion model on an offline trajectory dataset. We introduce a new SE(3)xZxSn-equivariant diffusion model that supports multiple representations. We integrate this model in a planning loop, where conditioning and classifier guidance let us softly break the symmetry for specific tasks as needed. On object manipulation and navigation tasks, EDGI is substantially more sample efficient and generalizes better across the symmetry group than non-equivariant models.  ( 2 min )

The recently proposed generative flow networks (GFlowNets) are a method of training a policy to sample compositional discrete objects with probabilities proportional to a given reward via a sequence of actions. GFlowNets exploit the sequential nature of the problem, drawing parallels with reinforcement learning (RL). Our work extends the connection between RL and GFlowNets to a general case. We demonstrate how the task of learning a generative flow network can be efficiently redefined as an entropy-regularized RL problem with a specific reward and regularizer structure. Furthermore, we illustrate the practical efficiency of this reformulation by applying standard soft RL algorithms to GFlowNet training across several probabilistic modeling tasks. Contrary to previously reported results, we show that entropic RL approaches can be competitive against established GFlowNet training methods. This perspective opens a direct path for integrating reinforcement learning principles into the realm of generative flow networks.  ( 2 min )

Inference on modern Bayesian Neural Networks (BNNs) often relies on a variational inference treatment, imposing violated assumptions of independence and the form of the posterior. Traditional MCMC approaches avoid these assumptions at the cost of increased computation due to its incompatibility to subsampling of the likelihood. New Piecewise Deterministic Markov Process (PDMP) samplers permit subsampling, though introduce a model specific inhomogenous Poisson Process (IPPs) which is difficult to sample from. This work introduces a new generic and adaptive thinning scheme for sampling from these IPPs, and demonstrates how this approach can accelerate the application of PDMPs for inference in BNNs. Experimentation illustrates how inference with these methods is computationally feasible, can improve predictive accuracy, MCMC mixing performance, and provide informative uncertainty measurements when compared against other approximate inference schemes.  ( 2 min )

Deep discriminative approaches like random forests and deep neural networks have recently found applications in many important real-world scenarios. However, deploying these learning algorithms in safety-critical applications raises concerns, particularly when it comes to ensuring confidence calibration for both in-distribution and out-of-distribution data points. Many popular methods for in-distribution (ID) calibration, such as isotonic regression and Platt's sigmoidal regression, exhibit excellent ID calibration performance but often at the cost of classification accuracy. Moreover, these methods are not calibrated for the entire feature space, leading to overconfidence in the case of out-of-distribution (OOD) samples. In this paper, we leveraged the fact that deep models, including both random forests and deep-nets, learn internal representations which are unions of polytopes with affine activation functions to conceptualize them both as partitioning rules of the feature space. We replace the affine function in each polytope populated by the training data with a Gaussian kernel. We propose sufficient conditions for our proposed methods to be consistent estimators of the corresponding class conditional densities. Moreover, our experiments on both tabular and vision benchmarks show that the proposed approaches obtain well-calibrated posteriors while mostly preserving or improving the classification accuracy of the original algorithm for in-distribution region, and extrapolates beyond the training data to handle out-of-distribution inputs appropriately.  ( 3 min )

We introduce Cosmos, a framework for object-centric world modeling that is designed for compositional generalization (CG), i.e., high performance on unseen input scenes obtained through the composition of known visual "atoms." The central insight behind Cosmos is the use of a novel form of neurosymbolic grounding. Specifically, the framework introduces two new tools: (i) neurosymbolic scene encodings, which represent each entity in a scene using a real vector computed using a neural encoder, as well as a vector of composable symbols describing attributes of the entity, and (ii) a neurosymbolic attention mechanism that binds these entities to learned rules of interaction. Cosmos is end-to-end differentiable; also, unlike traditional neurosymbolic methods that require representations to be manually mapped to symbols, it computes an entity's symbolic attributes using vision-language foundation models. Through an evaluation that considers two different forms of CG on an established blocks-pushing domain, we show that the framework establishes a new state-of-the-art for CG in world modeling.  ( 2 min )

Whether class labels in a given data set correspond to meaningful clusters is crucial for the evaluation of clustering algorithms using real-world data sets. This property can be quantified by separability measures. A review of the existing literature shows that neither classification-based complexity measures nor cluster validity indices (CVIs) adequately incorporate the central aspects of separability for density-based clustering: between-class separation and within-class connectedness. A newly developed measure (density cluster separability index, DCSI) aims to quantify these two characteristics and can also be used as a CVI. Extensive experiments on synthetic data indicate that DCSI correlates strongly with the performance of DBSCAN measured via the adjusted rand index (ARI) but lacks robustness when it comes to multi-class data sets with overlapping classes that are ill-suited for density-based hard clustering. Detailed evaluation on frequently used real-world data sets shows that DCSI can correctly identify touching or overlapping classes that do not form meaningful clusters.  ( 2 min )

Compressing neural networks is a key step when deploying models for real-time or embedded applications. Factorizing the model's matrices using low-rank approximations is a promising method for achieving compression. While it is possible to set the rank before training, this approach is neither flexible nor optimal. In this work, we propose a post-training rank-selection method called Rank-Tuning that selects a different rank for each matrix. Used in combination with training adaptations, our method achieves high compression rates with no or little performance degradation. Our numerical experiments on signal processing tasks show that we can compress recurrent neural networks up to 14x with at most 1.4% relative performance reduction.  ( 2 min )

Many modern statistical analysis and machine learning applications require training models on sensitive user data. Differential privacy provides a formal guarantee that individual-level information about users does not leak. In this framework, randomized algorithms inject calibrated noise into the confidential data, resulting in privacy-protected datasets or queries. However, restricting access to only the privatized data during statistical analysis makes it computationally challenging to perform valid inferences on parameters underlying the confidential data. In this work, we propose simulation-based inference methods from privacy-protected datasets. Specifically, we use neural conditional density estimators as a flexible family of distributions to approximate the posterior distribution of model parameters given the observed private query results. We illustrate our methods on discrete time-series data under an infectious disease model and on ordinary linear regression models. Illustrating the privacy-utility trade-off, our experiments and analysis demonstrate the necessity and feasibility of designing valid statistical inference procedures to correct for biases introduced by the privacy-protection mechanisms.  ( 2 min )

The key challenge underlying machine learning is generalisation to new data. This work studies generalisation for datasets consisting of related tasks that may differ in causal mechanisms. For example, observational medical data for complex diseases suffers from heterogeneity in causal mechanisms of disease across patients, creating challenges for machine learning algorithms that need to generalise to new patients outside of the training dataset. Common approaches for learning supervised models with heterogeneous datasets include learning a global model for the entire dataset, learning local models for each tasks' data, or utilising hierarchical, meta-learning and multi-task learning approaches to learn how to generalise from data pooled across multiple tasks. In this paper we propose causal similarity-based hierarchical Bayesian models to improve generalisation to new tasks by learning how to pool data from training tasks with similar causal mechanisms. We apply this general modelling principle to Bayesian neural networks and compare a variety of methods for estimating causal task similarity (for both known and unknown causal models). We demonstrate the benefits of our approach and applicability to real world problems through a range of experiments on simulated and real data.  ( 2 min )

We propose in this paper, STANLEY, a STochastic gradient ANisotropic LangEvin dYnamics, for sampling high dimensional data. With the growing efficacy and potential of Energy-Based modeling, also known as non-normalized probabilistic modeling, for modeling a generative process of different natures of high dimensional data observations, we present an end-to-end learning algorithm for Energy-Based models (EBM) with the purpose of improving the quality of the resulting sampled data points. While the unknown normalizing constant of EBMs makes the training procedure intractable, resorting to Markov Chain Monte Carlo (MCMC) is in general a viable option. Realizing what MCMC entails for the EBM training, we propose in this paper, a novel high dimensional sampling method, based on an anisotropic stepsize and a gradient-informed covariance matrix, embedded into a discretized Langevin diffusion. We motivate the necessity for an anisotropic update of the negative samples in the Markov Chain by the nonlinearity of the backbone of the EBM, here a Convolutional Neural Network. Our resulting method, namely STANLEY, is an optimization algorithm for training Energy-Based models via our newly introduced MCMC method. We provide a theoretical understanding of our sampling scheme by proving that the sampler leads to a geometrically uniformly ergodic Markov Chain. Several image generation experiments are provided in our paper to show the effectiveness of our method.  ( 2 min )

Due to the increasing use of Machine Learning models in high stakes decision making settings, it has become increasingly important to have tools to understand how models arrive at decisions. Assuming a trained Supervised Classification model, explanations can be obtained via counterfactual analysis: a counterfactual explanation of an instance indicates how this instance should be minimally modified so that the perturbed instance is classified in the desired class by the Machine Learning classification model. Most of the Counterfactual Analysis literature focuses on the single-instance single-counterfactual setting, in which the analysis is done for one single instance to provide one single explanation. Taking a stakeholder's perspective, in this paper we introduce the so-called collective counterfactual explanations. By means of novel Mathematical Optimization models, we provide a counterfactual explanation for each instance in a group of interest, so that the total cost of the perturbations is minimized under some linking constraints. Making the process of constructing counterfactuals collective instead of individual enables us to detect the features that are critical to the entire dataset to have the individuals classified in the desired class. Our methodology allows for some instances to be treated individually, performing the collective counterfactual analysis for a fraction of records of the group of interest. This way, outliers are identified and handled appropriately. Under some assumptions on the classifier and the space in which counterfactuals are sought, finding collective counterfactuals is reduced to solving a convex quadratic linearly constrained mixed integer optimization problem, which, for datasets of moderate size, can be solved to optimality using existing solvers. The performance of our approach is illustrated on real-world datasets, demonstrating its usefulness.  ( 3 min )

The training and generalization dynamics of the Transformer's core mechanism, namely the Attention mechanism, remain under-explored. Besides, existing analyses primarily focus on single-head attention. Inspired by the demonstrated benefits of overparameterization when training fully-connected networks, we investigate the potential optimization and generalization advantages of using multiple attention heads. Towards this goal, we derive convergence and generalization guarantees for gradient-descent training of a single-layer multi-head self-attention model, under a suitable realizability condition on the data. We then establish primitive conditions on the initialization that ensure realizability holds. Finally, we demonstrate that these conditions are satisfied for a simple tokenized-mixture model. We expect the analysis can be extended to various data-model and architecture variations.  ( 2 min )

In theoretical ML, the teacher-student paradigm is often employed as an effective metaphor for real-life tuition. The above scheme proves particularly relevant when the student network is overparameterized as compared to the teacher network. Under these operating conditions, it is tempting to speculate that the student ability to handle the given task could be eventually stored in a sub-portion of the whole network. This latter should be to some extent reminiscent of the frozen teacher structure, according to suitable metrics, while being approximately invariant across different architectures of the student candidate network. Unfortunately, state-of-the-art conventional learning techniques could not help in identifying the existence of such an invariant subnetwork, due to the inherent degree of non-convexity that characterizes the examined problem. In this work, we take a leap forward by proposing a radically different optimization scheme which builds on a spectral representation of the linear transfer of information between layers. The gradient is hence calculated with respect to both eigenvalues and eigenvectors with negligible increase in terms of computational and complexity load, as compared to standard training algorithms. Working in this framework, we could isolate a stable student substructure, that mirrors the true complexity of the teacher in terms of computing neurons, path distribution and topological attributes. When pruning unimportant nodes of the trained student, as follows a ranking that reflects the optimized eigenvalues, no degradation in the recorded performance is seen above a threshold that corresponds to the effective teacher size. The observed behavior can be pictured as a genuine second-order phase transition that bears universality traits.  ( 3 min )

We commonly encounter the problem of identifying an optimally weight adjusted version of the empirical distribution of observed data, adhering to predefined constraints on the weights. Such constraints often manifest as restrictions on the moments, tail behaviour, shapes, number of modes, etc., of the resulting weight adjusted empirical distribution. In this article, we substantially enhance the flexibility of such methodology by introducing a nonparametrically imbued distributional constraints on the weights, and developing a general framework leveraging the maximum entropy principle and tools from optimal transport. The key idea is to ensure that the maximum entropy weight adjusted empirical distribution of the observed data is close to a pre-specified probability distribution in terms of the optimal transport metric while allowing for subtle departures. The versatility of the framework is demonstrated in the context of three disparate applications where data re-weighting is warranted to satisfy side constraints on the optimization problem at the heart of the statistical task: namely, portfolio allocation, semi-parametric inference for complex surveys, and ensuring algorithmic fairness in machine learning algorithms.  ( 2 min )

Manifold learning flows are a class of generative modelling techniques that assume a low-dimensional manifold description of the data. The embedding of such manifold into the high-dimensional space of the data is achieved via learnable invertible transformations. Therefore, once the manifold is properly aligned via a reconstruction loss, the probability density is tractable on the manifold and maximum likelihood can be used optimize the network parameters. Naturally, the lower-dimensional representation of the data requires an injective-mapping. Recent approaches were able to enforce that density aligns with the modelled manifold, while efficiently calculating the density volume-change term when embedding to the higher-dimensional space. However, unless the injective-mapping is analytically predefined, the learned manifold is not necessarily an efficient representation of the data. Namely, the latent dimensions of such models frequently learn an entangled intrinsic basis with degenerate information being stored in each dimension. Alternatively, if a locally orthogonal and/or sparse basis is to be learned, here coined canonical intrinsic basis, it can serve in learning a more compact latent space representation. Towards this end, we propose a canonical manifold learning flow method, where a novel optimization objective enforces the transformation matrix to have few prominent and orthogonal basis functions. Canonical manifold flow yields a more efficient use of the latent space, automatically generating fewer prominent and distinct dimensions to represent data, and consequently a better approximation of target distributions than other manifold flow methods in most experiments we conducted, resulting in lower FID scores.  ( 2 min )

Entropy maximization and free energy minimization are general physical principles for modeling the dynamics of various physical systems. Notable examples include modeling decision-making within the brain using the free-energy principle, optimizing the accuracy-complexity trade-off when accessing hidden variables with the information bottleneck principle (Tishby et al., 2000), and navigation in random environments using information maximization (Vergassola et al., 2007). Built on this principle, we propose a new class of bandit algorithms that maximize an approximation to the information of a key variable within the system. To this end, we develop an approximated analytical physics-based representation of an entropy to forecast the information gain of each action and greedily choose the one with the largest information gain. This method yields strong performances in classical bandit settings. Motivated by its empirical success, we prove its asymptotic optimality for the two-armed bandit problem with Gaussian rewards. Owing to its ability to encompass the system's properties in a global physical functional, this approach can be efficiently adapted to more complex bandit settings, calling for further investigation of information maximization approaches for multi-armed bandit problems.  ( 2 min )

We study the performance of empirical risk minimization on the $p$-norm linear regression problem for $p \in (1, \infty)$. We show that, in the realizable case, under no moment assumptions, and up to a distribution-dependent constant, $O(d)$ samples are enough to exactly recover the target. Otherwise, for $p \in [2, \infty)$, and under weak moment assumptions on the target and the covariates, we prove a high probability excess risk bound on the empirical risk minimizer whose leading term matches, up to a constant that depends only on $p$, the asymptotically exact rate. We extend this result to the case $p \in (1, 2)$ under mild assumptions that guarantee the existence of the Hessian of the risk at its minimizer.  ( 2 min )

The quality of explanations for the predictions of complex machine learning predictors is often measured using insertion and deletion metrics, which assess the faithfulness of the explanations, i.e., how correctly the explanations reflect the predictor's behavior. To improve the faithfulness, we propose insertion/deletion metric-aware explanation-based optimization (ID-ExpO), which optimizes differentiable predictors to improve both insertion and deletion scores of the explanations while keeping their predictive accuracy. Since the original insertion and deletion metrics are indifferentiable with respect to the explanations and directly unavailable for gradient-based optimization, we extend the metrics to be differentiable and use them to formalize insertion and deletion metric-based regularizers. The experimental results on image and tabular datasets show that the deep neural networks-based predictors fine-tuned using ID-ExpO enable popular post-hoc explainers to produce more faithful and easy-to-interpret explanations while keeping high predictive accuracy.  ( 2 min )

In the realm of deep learning, transformers have emerged as a dominant architecture, particularly in natural language processing tasks. However, with their widespread adoption, concerns regarding the security and privacy of the data processed by these models have arisen. In this paper, we address a pivotal question: Can the data fed into transformers be recovered using their attention weights and outputs? We introduce a theoretical framework to tackle this problem. Specifically, we present an algorithm that aims to recover the input data $X \in \mathbb{R}^{d \times n}$ from given attention weights $W = QK^\top \in \mathbb{R}^{d \times d}$ and output $B \in \mathbb{R}^{n \times n}$ by minimizing the loss function $L(X)$. This loss function captures the discrepancy between the expected output and the actual output of the transformer. Our findings have significant implications for the Localized Layer-wise Mechanism (LLM), suggesting potential vulnerabilities in the model's design from a security and privacy perspective. This work underscores the importance of understanding and safeguarding the internal workings of transformers to ensure the confidentiality of processed data.  ( 2 min )

Stochastic processes defined on integer valued state spaces are popular within the physical and biological sciences. These models are necessary for capturing the dynamics of small systems where the individual nature of the populations cannot be ignored and stochastic effects are important. The inference of the parameters of such models, from time series data, is difficult due to intractability of the likelihood; current methods, based on simulations of the underlying model, can be so computationally expensive as to be prohibitive. In this paper we construct a neural likelihood approximation for integer valued time series data using causal convolutions, which allows us to evaluate the likelihood of the whole time series in parallel. We demonstrate our method by performing inference on a number of ecological and epidemiological models, showing that we can accurately approximate the true posterior while achieving significant computational speed ups in situations where current methods struggle.  ( 2 min )

Score-based generative models (SGMs) sample from a target distribution by iteratively transforming noise using the score function of the perturbed target. For any finite training set, this score function can be evaluated in closed form, but the resulting SGM memorizes its training data and does not generate novel samples. In practice, one approximates the score by training a neural network via score-matching. The error in this approximation promotes generalization, but neural SGMs are costly to train and sample, and the effective regularization this error provides is not well-understood theoretically. In this work, we instead explicitly smooth the closed-form score to obtain an SGM that generates novel samples without training. We analyze our model and propose an efficient nearest-neighbor-based estimator of its score function. Using this estimator, our method achieves sampling times competitive with neural SGMs while running on consumer-grade CPUs.  ( 2 min )

We initiate a novel approach to explain the out of sample performance of random forest (RF) models by exploiting the fact that any RF can be formulated as an adaptive weighted K nearest-neighbors model. Specifically, we use the proximity between points in the feature space learned by the RF to re-write random forest predictions exactly as a weighted average of the target labels of training data points. This linearity facilitates a local notion of explainability of RF predictions that generates attributions for any model prediction across observations in the training set, and thereby complements established methods like SHAP, which instead generates attributions for a model prediction across dimensions of the feature space. We demonstrate this approach in the context of a bond pricing model trained on US corporate bond trades, and compare our approach to various existing approaches to model explainability.  ( 2 min )

High-dimensional longitudinal data is increasingly used in a wide range of scientific studies. However, there are few statistical methods for high-dimensional linear mixed models (LMMs), as most Bayesian variable selection or penalization methods are designed for independent observations. Additionally, the few available software packages for high-dimensional LMMs suffer from scalability issues. This work presents an efficient and accurate Bayesian framework for high-dimensional LMMs. We use empirical Bayes estimators of hyperparameters for increased flexibility and an Expectation-Conditional-Minimization (ECM) algorithm for computationally efficient maximum a posteriori probability (MAP) estimation of parameters. The novelty of the approach lies in its partitioning and parameter expansion as well as its fast and scalable computation. We illustrate Linear Mixed Modeling with PaRtitiOned empirical Bayes ECM (LMM-PROBE) in simulation studies evaluating fixed and random effects estimation along with computation time. A real-world example is provided using data from a study of lupus in children, where we identify genes and clinical factors associated with a new lupus biomarker and predict the biomarker over time.  ( 2 min )

Molecular language modeling is an effective approach to generating novel chemical structures. However, these models do not \emph{a priori} encode certain preferences a chemist may desire. We investigate the use of fine-tuning using Direct Preference Optimization to better align generated molecules with chemist preferences. Our findings suggest that this approach is simple, efficient, and highly effective.  ( 2 min )

Hello, I would love to have an LLm that can provide answers (in chat format) based some of the sql db data we have. Want it for an internal company project. I am by no means an expert but decent in programming and want to build a system to get answers in chat format. My understanding is that training LLMs ground up is prohibitively expensive and langchains are sort of hybrid , efficient solutions. Please suggest any other solutions. Also would Langchain being a company and not open source pose a problem in terms of copyrights? Thanks! submitted by /u/betelgeuseian [link] [comments]  ( 9 min )

Paper: https://arxiv.org/abs/2310.08560 Github: https://github.com/cpacker/MemGPT Blog: https://memgpt.ai/ Youtube: https://youtu.be/QQ2QOPWZKVc?si=_bSSXU9EQE0FP64h MemGPT 🧠 Giving AI Unlimited Prompt Size (Big Step Towards AGI?) by Metthew Berman / Must watch and he also explains how to install it! Overview LLMs are increasingly being used for perpetual chats Limited context lengths makes perpetual chat challenging MemGPT manages a virtual context (inspired by virtual memory in operating systems) to create unbounded LLM context With MemGPT, we demonstrate that LLMs can be taught to manage their own memory! Abstract: Large language models (LLMs) have revolutionized AI, but are constrained by limited context windows, hindering their utility in tasks like extended conversa…  ( 9 min )

Hi, I am a mac user. I am trying to use whisper.cpp downloaded from its github file. I don't know much about phyton or coding so I basically followed this guide to install and use it. I downloaded the large model to try it. I am using it for non-English languages and I want to use it for language learning purposes so I can understand what is being said in an Instagram story or a Youtube video (without subtitles) or a tv series or an extract of movie etc. I was using Macwhisper but I wanted to try the pro features and I don't want to pay for it (for now) and try the pro models for non-English languages. My question is: all of my files that I want to transcribe are video files with .mp4 extension. Can I also transcribe those with whisper? If not, and if I can only transcribe audio files, can it be .mp3? I understand that I need to install and use ffmpeg. Does it support mp3? Also, as I understand, the transcripted text will appear in the terminal. Can I export it in -srt or pdf? Thanks submitted by /u/toughytough [link] [comments]  ( 9 min )

I've watched an interesting video: Deriving the Ultimate Neural Network Architecture from Scratch. It's about how to come up to the transformer architecture when you have an understanding of CNNs. The crux of it is an idea of pairwise convolutional layers. The first layer applies not to the sequence of words itself, but to all pairs of words in the sentence. This ensures that each relation of words that are far from each other is taken into account. The next convolutional layer applies to all pairs of results of the previous one. This way longer subsequences of words are factored in. pairs of words My question is: are there any articles on how transformers were invented? I see a lot of explanations of the original paper, but at best they all answer the question how transformers work. But why is the architecture the way it is? Was it discovered like the video describes? Or the path was more convoluted? I'd like to know more about this connection. Anyway, it would be great to figure out in all details how these pairwise layers are related to the concepts of query, key, and value. Here's what the author of the video wrote in comments: Yeah it's a term I made up so you won't find it in any sources, sorry about that. Usually sources will just talk about self attention in terms of key, query and value lookups, so you can look at those to get a more detailed understanding of the transformer. The value transform is equivalent to the linear representation function I use in the pairwise convolution, the key and query attention scores are equivalent to the bi-linear form scoring function I use (with the bi-linear form weight matrix given by Q^TK). I chose to use this unusual terminology because, personally, I feel the key, query and value terminology comes out of nowhere, and I wanted to connect the transformer more directly to its predecessor (the CNN). ​ submitted by /u/Veson [link] [comments]  ( 10 min )

I'm trying to fit a multivariate LSTM model to time series data to predict future values for one relatively noisy series. I noticed that the the loss (mse in this case) is pretty high given that the data has been standardized beforehand. So I really have two questions: why is the mse so high and is the learning curve indicative of any obvious problems? Thank you! https://preview.redd.it/r9bel6p7kfvb1.png?width=547&format=png&auto=webp&s=4eee53aa8005da8a89f330f6e98fe6cadde3467e submitted by /u/DifferenceUnhappy393 [link] [comments]  ( 9 min )

Sutton and Barto’s textbook mentions that combing off-policy learning, bootstrapping, and function approximation leads to extreme instability and should be avoided. Yet when I encounter a reinforcement problem in the wild and look how people go about solving it, if someone’s solution involves bootstrapping more often than not it’s some variation of deep Q-learning. Why is this? submitted by /u/BiasedEstimators [link] [comments]

Hey everyone, I'm learning ML but i'm barely scratching the terminologies. 2 years ago I couldn't code anything but with school (python,sql and R) I learned fundamentals. I also have access to code academy. My current program is very machine learning/deep learning focused. On the side I DM a d&d game. Within the context of the world (eberron) robots are common. With my ADHD and being a new DM I want to outsource lore questions might have (that I would have to look up and slow down the game). The concept is to have a GUI and have the player interact with the chat bot. I've gotten to a proof of concept workflow. On Google colab. Thanks to langchain I managed to ingest pdfs and a url. Make then a directory, Embedded the text, bring it into a vector dB. Have the llm pull from the vector. Answer the question. Now I don't know what to do. I tried to bring the colab notebook onto Google cloud. But now cloud is becoming a rabbit home with vertex and docAI...and I don't want to deep dive into that, if it's a outside the scope of this "project" I'd appreciate any advice, links...etc. I got a limited success in botpress using a single pdf. It works but feel unsatisfying. N8N looks promising but if it's not intuitive then I don't want to go down that road. If I posted in the wrong group please direct me to the correct one. submitted by /u/work929 [link] [comments]  ( 9 min )

https://arxiv.org/abs/2310.10638 "Large language models (LMs) are currently trained to predict tokens given document prefixes, enabling them to directly perform long-form generation and prompting-style tasks which can be reduced to document completion. Existing pretraining pipelines train LMs by concatenating random sets of short documents to create input contexts but the prior documents provide no signal for predicting the next document. We instead present In-Context Pretraining, a new approach where language models are pretrained on a sequence of related documents, thereby explicitly encouraging them to read and reason across document boundaries. We can do In-Context Pretraining by simply changing the document ordering so that each context contains related documents, and directly applying existing pretraining pipelines. However, this document sorting problem is challenging. There are billions of documents and we would like the sort to maximize contextual similarity for every document without repeating any data. To do this, we introduce approximate algorithms for finding related documents with efficient nearest neighbor search and constructing coherent input contexts with a graph traversal algorithm. Our experiments show In-Context Pretraining offers a simple and scalable approach to significantly enhance LMs'performance: we see notable improvements in tasks that require more complex contextual reasoning, including in-context learning (+8%), reading comprehension (+15%), faithfulness to previous contexts (+16%), long-context reasoning (+5%), and retrieval augmentation (+9%)." submitted by /u/Parking-Priority6217 [link] [comments]  ( 9 min )

Greetings, I'm on my 2nd year of College (Artificial Intelligence bachelors degree), and currently making a group project that will require machine learning. The project consists of managing and regulating the conditions (temperature, humidity, lightning, etc.) of the environment that surrounds important products (vaccines, human organs, etc.) during their transportation, using sensors implemented in their transportation box. For that being possible, our group was planning to use a predictive model using machine learning, to prevent cases such as the exposure of inappropriate temperature levels, that could damage the product, and subsequently taking the appropriate measures to improve the environment, before it reaches such dangerous scenarios. Therefore, I would like to know which tools and skills will be needed and helpful in order to achieve such goal. If you have any advice, that'll be very much appreciated. :) submitted by /u/Storm2003 [link] [comments]  ( 9 min )

Researchers propose a new AI system called 3D-GPT that creates 3D models by combining natural language instructions and agents specialized for working with existing 3D modeling tools. 3D-GPT has predefined functions that make 3D shapes, and it tweaks parameters to build scenes. The key is getting the AI to understand instructions and pick the right tools. It has three main agents: A dispatcher that parses the text and picks generation functions A conceptualizer that adds details missing from the description A modeler that sets parameters and outputs code to drive 3D software By breaking modeling work down into steps, the agents can collab to match the descriptions. This is sort of like how a 3D modeling team of humans would work. The paper authors show it making simple scenes like "lush meadow with flowers" that fit the text. It also modifies scenes appropriately when given new instructions. I include some gifs of example outputs in my full summary. They look pretty good - I would say 2005-quality graphics. There are limits. It fully relies on existing generators, so quality is capped. Details and curves are iffy. It resorts to default shapes often instead of true understanding. And I doubt the verts and textures are well-optimized. The agent architecture seems to be really popular right now. This one shows some planning skills, which could extend to more creative tasks someday. TLDR: AI agents can team up to generate 3D models from text instructions. Works to some degree but limitations remain. Full summary. Paper here. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

Link: https://ojs.aaai.org/index.php/AAAI/article/view/25726/25498 Abstract: We study the combinatorial assignment domain, which includes combinatorial auctions and course allocation. The main challenge in this domain is that the bundle space grows exponentially in the number of items. To address this, several papers have recently proposed machine learning-based preference elicitation algorithms that aim to elicit only the most important information from agents. However, the main shortcoming of this prior work is that it does not model a mechanism's uncertainty over values for not yet elicited bundles. In this paper, we address this shortcoming by presenting a Bayesian optimization-based combinatorial assignment (BOCA) mechanism. Our key technical contribution is to integrate a method for capturing model uncertainty into an iterative combinatorial auction mechanism. Concretely, we design a new method for estimating an upper uncertainty bound that can be used to define an acquisition function to determine the next query to the agents. This enables the mechanism to properly explore (and not just exploit) the bundle space during its preference elicitation phase. We run computational experiments in several spectrum auction domains to evaluate BOCA's performance. Our results show that BOCA achieves higher allocative efficiency than state-of-the-art approaches. https://preview.redd.it/aeo36u3wldvb1.png?width=1288&format=png&auto=webp&s=2982547f8af51ed7195f49dbec9359fecba1693f ​ submitted by /u/Yossarian_1234 [link] [comments]  ( 9 min )

So a lot of multimodal models I've seen use a linear layer to transform encoded image/video/audio into the multimodal LLMs embedding space. This makes sense for the input, but how would output work? Normally you use a layer to convert the embedding to a SoftMax of probabilities of possible output tokens. This makes sense for discrete outputs like tokens but not for continuous outputs like images or audio. ​ submitted by /u/30299578815310 [link] [comments]  ( 9 min )

The title says it all. I agree what they did is incredible and literally changed AI landscape in last couple of years. But I’m getting tired of everyone acting like OpenAI is the only one doing great research. The twit-fluencers praising even the slightest peep from them. I don’t understand this fanaticism in AI community. There are smart researchers doing smart things all over the world. But they don’t even get a fraction of appreciation they deserve. And the strangest thing of all, ChatGPT is used as oracle to evaluate models in research papers. Consistency models are extremely meh and if it did not come out of openAI, people would’ve forgotten them a long time ago! Edit 1: I’m in grad school and that’s all a lot of students around me talk about/ chase. I want to work on a bit more fundamental problems, but I feel like I’m being left behind. Edit 2: This post is mostly a rant about academics obsessed with OpenAI research/products and LLMs. submitted by /u/mildlyphd [link] [comments]  ( 9 min )